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Dissertations / Theses on the topic 'Face detection'
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1
Espinosa-Romero, Arturo. "Situated face detection." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/6667.
Full textAbstract:
In the last twenty years, important advances have been made in the field of automatic face processing, given the importance of human faces for personal identification, emotional expression and verbal and non verbal communication. The very first step in a face processing algorithm is the detection of faces; while this is a trivial problem in controlled environments, the detection of faces in real environments is still a challenging task. Until now, the most successful approaches for face detection represent the face as a grey-level pattern, and the problem itself is considered as the classification between "face" and "non-face" patterns. Satisfactory results have been achieved in this area. The main disadvantage is that an exhaustive search has to be done on each image in order to locate the faces. This search normally involves testing every single position on the image at different scales, and although this does not represent an important drawback in off-line face processing systems, in those cases where a real-time response is needed it is still a problem. In the different proposed methods for face detection, the "observer" is a disembodied entity, which holds no relationship with the observed scene. This thesis presents a framework for an efficient location of faces in real scenes, in which, by considering both the observer to be situated in the world, and the relationships that hold between the two, a set of constraints in the search space can be defined. The constraints rely on two main assumptions; first, the observer can purposively interact with the world (i.e. change its position relative to the observed scene) and second, the camera is fully calibrated. The first source constraint is the structural information about the observer environment, represented as a depth map of the scene in front of the camera. From this representation the search space can be constrained in terms of the range of scales where a face might be found as different positions in the image. The second source of constraint is the geometrical relationship between the camera and the scene, which allows us to project a model of the subject into the scene in order to eliminate those areas where faces are unlikely to be found. In order to test the proposed framework, a system based on the premises stated above was constructed. It is based on three different modules: a face/non-face classifier, a depth estimation module and a search module. The classifier is composed of a set of convolutional neural networks (CNN) that were trained to differentiate between face and non-face patterns, the depth estimation modules uses a multilevel algorithm to compute the scene depth map from a sequence of images captured the depth information and the subject model into the image where the search will be performed in order to constrain the search space. Finally, the proposed system was validated by running a set of experiments on the individual modules and then on the whole system.
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Mäkelä, J. (Jussi). "GPU accelerated face detection." Master's thesis, University of Oulu, 2013. http://urn.fi/URN:NBN:fi:oulu-201303181103.
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Graphics processing units have massive parallel processing capabilities, and there is a growing interest in utilizing them for generic computing. One area of interest is computationally heavy computer vision algorithms, such as face detection and recognition. Face detection is used in a variety of applications, for example the autofocus on cameras, face and emotion recognition, and access control. In this thesis, the face detection algorithm was accelerated with GPU using OpenCL. The goal was to gain performance benefit while keeping the implementations functionally equivalent. The OpenCL version was based on optimized reference implementation. The possibilities and challenges in accelerating different parts of the algorithm were studied. The reference and the accelerated implementations are depicted in detail, and performance is compared. The performance was evaluated by runtimes with three sets of four different sized images, and three additional images presenting special cases. The tests were run with two differently set-up computers. From the results, it can be seen that face detection is well suited for GPU acceleration; that is the algorithm is well parallelizable and can utilize efficient texture processing hardware. There are delays related in initializing the OpenCL platform which mitigate the benefit to some degree. The accelerated implementation was found to deliver equal or lower performance when there was little computation; that is the image was small or easily analyzed. With bigger and more complex images, the accelerated implementation delivered good performance compared to reference implementation. In future work, there should be some method of mitigating delays introduced by the OpenCL initialization. This work will have interest in the future when OpenCL acceleration becomes available on mobile phones<br>Grafiikkaprosessorit kykenevät massiiviseen rinnakkaislaskentaan ja niiden käyttö yleiseen laskentaan on kasvava kiinnostuksen aihe. Eräs alue missä kiihdytyksen käytöstä on kiinnostuttu on laskennallisesti raskaat konenäköalgoritmit kuten kasvojen ilmaisu ja tunnistus. Kasvojen ilmaisua käytetään useissa sovelluksissa, kuten kameroiden automaattitarkennuksessa, kasvojen ja tunteiden tunnistuksessa sekä kulun valvonnassa. Tässä työssä kasvojen ilmaisualgoritmia kiihdytettiin grafiikkasuorittimella käyttäen OpenCL-rajapintaa. Työn tavoite oli parantunut suorituskyky kuitenkin niin että implementaatiot pysyivät toiminnallisesti samanlaisina. OpenCL-versio perustui optimoituun verrokki-implementaatioon. Algoritmin eri vaiheiden kiihdytyksen mahdollisuuksia ja haasteita on tutkittu. Kiihdytetty- ja verrokki-implementaatio kuvaillaan ja niiden välistä suorituskykyeroa vertaillaan. Suorituskykyä arvioitiin ajoaikojen perusteella. Testeissä käytettiin kolmea kuvasarjaa joissa jokaisessa oli neljä eri kokoista kuvaa sekä kolmea lisäkuvaa jotka kuvastivat erikoistapauksia. Testit ajettiin kahdella erilailla varustellulla tietokoneella. Tuloksista voidaan nähdä että kasvojen ilmaisu soveltuu hyvin GPU kiihdytykseen, sillä algoritmin pystyy rinnakkaistamaan ja siinä pystyy käyttämään tehokasta tekstuurinkäsittelylaitteistoa. OpenCL-ympäristön alustaminen aiheuttaa viivettä joka vähentää jonkin verran suorituskykyetua. Testeissä todettiin kiihdytetyn implementaation antavan saman suuruisen tai jopa pienemmän suorituskyvyn kuin verrokki-implementaatio sellaisissa tapauksissa, joissa laskentaa oli vähän johtuen joko pienestä tai helposti käsiteltävästä kuvasta. Toisaalta kiihdytetyn implementaation suorituskyky oli hyvä verrattuna verrokki-implementaatioon kun käytettiin suuria ja monimutkaisia kuvia. Tulevaisuudessa OpenCL-ympäristön alustamisen aiheuttamat viivettä tulisi saada vähennettyä. Tämä työ on kiinnostava myös tulevaisuudessa kun OpenCL-kiihdytys tulee mahdolliseksi matkapuhelimissa
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Costa, Rui Jorge Duarte. "Face detection and recognision." Master's thesis, Universidade de Aveiro, 2016. http://hdl.handle.net/10773/21683.
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Mestrado em Engenharia Eletrónica e Telecomunicações<br>Ultimamente, as redes de telecomunicações móveis estão a exigir cada vez maiores taxas de transferência de informação. Com este aumento, embora sejam usados códigos poderosos, também aumenta a largura de banda dos sinais a transmitir, bem como a sua frequência.
A maior frequência de operação, bem como a procura por sistemas mais eficientes, tem exigido progressos no que toca aos transístores utilizados nos amplificadores de potência de radio frequência (RF), uma vez que estes são componentes dominantes no rendimento de uma estação base de telecomunicações.
Com esta evolução, surgem novas tecnologias de transístores, como os GaN HEMT (do inglês, Gallium Nitride High Electron Mobility Transistor). Para conseguir prever e corrigir certos efeitos dispersivos que afetam estas novas tecnologias e para obter o amplificador mais eficiente para cada transístor usado, os projetistas de amplificadores necessitam cada vez mais de um modelo que reproduza fielmente o comportamento do dispositivo.
Durante este trabalho foi desenvolvido um sistema capaz de efetuar medidas pulsadas e de elevada exatidão a transístores, para que estes não sejam afetados, durante as medidas, por fenómenos de sobreaquecimento ou outro tipo de fenómenos dispersivos mais complexos presentes em algumas tecnologias. Desta forma, será possível caracterizar estes transístores para um estado pré determinado não só de temperatura, mas de todos os fenómenos presentes.
Ao longo do trabalho vai ser demostrado o projeto e a construção deste sistema, incluindo a parte de potência que será o principal foco do trabalho. Foi assim possível efetuar medidas pulsadas DC-IV e de parâmetros S (do inglês, Scattering) pulsados para vários pontos de polarização. Estas últimas foram conseguidas á custa da realização de um kit de calibração TRL. O interface gráfico com o sistema foi feito em Matlab, o que torna o sistema mais fácil de operar. Com as medidas resultantes pôde ser obtida uma primeira análise acerca da eficiência, ganho e potência máxima entregue pelo dispositivo. Mais tarde, com as mesmas medidas pôde ser obtido um modelo não linear completo do dispositivo, facilitando assim o projeto de amplificadores.<br>Lately, the wireless networks should feature higher data rates than ever. With this rise, although very powerful codification schemes are used, the bandwidth of the transmitted signals is rising, as well as the frequency.
Not only caused by this rise in frequency, but also by the growing need for more efficient systems, major advances have been made in terms of Radio Frequency (RF) Transistors that are used in Power Amplifiers (PAs), which are dominant components in terms of the total efficiency of base stations (BSS).
With this evolution, new technologies of transistors are being developed, such as the Gallium Nitride High Electron Mobility Transistor (GaN HEMT). In order to predict and correct some dispersive effects that affect these new technologies and obtain the best possible amplifier for each different transistor, the designers are relying more than ever in the models of the devices.
During this work, one system capable of performing very precise pulsed measurements on RF transistors was developed, so that they are not affected, during the measurements, by self-heating or other dispersive phenomena that are present in some technologies. Using these measurements it was possible to characterize these transistors for a pre-determined state of the temperature and all the other phenomena.
In this document, the design and assembly of the complete system will be analysed, with special attention to the higher power component. It will be possible to measure pulsed Direct Current Current-Voltage (DC-IV) behaviour and pulsed Scattering (S) parameters of the device for many different bias points. These latter ones were possible due to the development of one TRL calibration kit. The interface with the system is made using a graphical interface designed in Matlab, which makes it easier to use. With the resulting measurements, as a first step analysis, the maximum efficiency, gain and maximum delivered power of the device can be estimated. Later, with the same measurements, the complete non-linear model of the device can be obtained, allowing the designers to produce state-of-art RF PAs.
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Pavani, Sri-Kaushik. "Methods for face detection and adaptive face recognition." Doctoral thesis, Universitat Pompeu Fabra, 2010. http://hdl.handle.net/10803/7567.
Full textAbstract:
The focus of this thesis is on facial biometrics; specifically in the problems of face detection and face recognition. Despite intensive research over the last 20 years, the technology is not foolproof, which is why we do not see use of face recognition systems in critical sectors such as banking. In this thesis, we focus on three sub-problems in these two areas of research. Firstly, we propose methods to improve the speed-accuracy trade-off of the state-of-the-art face detector. Secondly, we consider a problem that is often ignored in the literature: to decrease the training time of the detectors. We propose two techniques to this end. Thirdly, we present a detailed large-scale study on self-updating face recognition systems in an attempt to answer if continuously changing facial appearance can be learnt automatically.<br>L'objectiu d'aquesta tesi és sobre biometria facial, específicament en els problemes de detecció de rostres i reconeixement facial. Malgrat la intensa recerca durant els últims 20 anys, la tecnologia no és infalible, de manera que no veiem l'ús dels sistemes de reconeixement de rostres en sectors crítics com la banca. En aquesta tesi, ens centrem en tres sub-problemes en aquestes dues àrees de recerca. En primer lloc, es proposa mètodes per millorar l'equilibri entre la precisió i la velocitat del detector de cares d'última generació. En segon lloc, considerem un problema que sovint s'ignora en la literatura: disminuir el temps de formació dels detectors. Es proposen dues tècniques per a aquest fi. En tercer lloc, es presenta un estudi detallat a gran escala sobre l'auto-actualització dels sistemes de reconeixement facial en un intent de respondre si el canvi constant de l'aparença facial es pot aprendre de forma automàtica.
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Day, Adam C. "Designing a face detection CAPTCHA." Morgantown, W. Va. : [West Virginia University Libraries], 2010. http://hdl.handle.net/10450/11036.
Full textAbstract:
Thesis (M.S.)--West Virginia University, 2010.<br>Title from document title page. Document formatted into pages; contains viii, 80 p. : ill. Includes abstract. Includes bibliographical references (p. 78-80).
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Lang, Andreas. "Face Detection using Swarm Intelligence." Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-64415.
Full textAbstract:
Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group
of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science,
particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying
structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J.
Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes
the ability to solve complex problems.
The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm
intelligence. The process developed for this purpose consists of a combination of various known structures, which are then
adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example
application program.
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McCarroll, Niall. "BioFace : bio-inspired face detection." Thesis, Ulster University, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.722684.
Full textAbstract:
The goal of face detection is to determine whether or not an image or video frame contains faces and, if present, return the number of instances of each face object and their location within an image space. Face detection is an important computer vision task as it is the building block for more sophisticated face processing algorithms such as face recognition and facial expression tracking. However, robust and reliable face detection in completely unconstrained settings remains a very challenging task. For example, while the human brain performs face detection and recognition robustly and with apparent ease, computer algorithms continue to find this a difficult task due to the huge variation of facial appearance in still images and video sequences. The existing literature documents extensive work on face detection utilising different classical machine learning and traditional algorithmic techniques. Given that challenges such as invariance to facial pose still remain with these traditional machine learning approaches, an exploration of biologically representative solutions that behave adaptively and autonomously through learning may help account for the well documented superior human and primate detection performance. In an effort to implement a more biologically plausible approach to invariant multi-view face detection, this thesis presents a novel hierarchical Spiking Neural Network (SNN) framework that adopts a hybrid approach to learning. This is achieved by combining a bottom-up unsupervised Spike-Timing Dependent Plasticity (STDP) feature extraction and filtering phase with a supervised feature selection process that provides feedback to the framework in an effort to select the most diagnostic neurons for accurate face detection. The detection accuracy of the hybrid system is further enhanced through two biologically plausible mechanisms of error control; namely threshold potential adaptation and spike latency thresholding. The broadly tuned behaviour of the neurons allows for a small but expressive set of multiview neurons to achieve efficient and robust detection for multi-view face poses. The merged, multi-view face detection system is further adapted through a competitive lateral inhibition mechanism to achieve accurate in-plane and out-of-plane face pose estimation.
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Mahmood, Muhammad Tariq. "Face Detection by Image Discriminating." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4352.
Full textAbstract:
Human face recognition systems have gained a considerable attention during last few years. There are very many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. Human face is non rigid and has very many variations regarding image conditions, size, resolution, poses and rotation. Its accurate and robust detection has been a challenge for the researcher. A number of methods and techniques are proposed but due to a huge number of variations no one technique is much successful for all kinds of faces and images. Some methods are exhibiting good results in certain conditions and others are good with different kinds of images. Image discriminating techniques are widely used for pattern and image analysis. Common discriminating methods are discussed.<br>SIPL, Mechatronics, GIST 1 Oryong-Dong, Buk-Gu, Gwangju, 500-712 South Korea tel. 0082-62-970-2997
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Lang, Andreas. "Face Detection using Swarm Intelligence." Technische Universität Chemnitz, 2010. https://monarch.qucosa.de/id/qucosa%3A19439.
Full textAbstract:
Groups of starlings can form impressive shapes as they travel northward together in the springtime. This is among a group
of natural phenomena based on swarm behaviour. The research field of artificial intelligence in computer science,
particularly the areas of robotics and image processing, has in recent decades given increasing attention to the underlying
structures. The behaviour of these intelligent swarms has opened new approaches for face detection as well. G. Beni and J.
Wang coined the term “swarm intelligence” to describe this type of group behaviour. In this context, intelligence describes
the ability to solve complex problems.
The objective of this project is to automatically find exactly one face on a photo or video material by means of swarm
intelligence. The process developed for this purpose consists of a combination of various known structures, which are then
adapted to the task of face detection. To illustrate the result, a 3D hat shape is placed on top of the face using an example
application program.:1 Introduction
1.1 Face Detection
1.2 Swarm Intelligence and Particle Swarm Optimisation Fundamentals
3 Face Detection by Means of Particle Swarm Optimisation
3.1 Swarms and Particles
3.2 Behaviour Patterns
3.2.1 Opportunism
3.2.2 Avoidance
3.2.3 Other Behaviour Patterns
3.3 Stop Criterion
3.4 Calculation of the Solution
3.5 Example Application
4 Summary and Outlook
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Husain, Benafsh Nadir. "Face Detection And Lip Localization." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/601.
Full textAbstract:
Integration of audio and video signals for automatic speech recognition has become an important field of study. The Audio-Visual Speech Recognition (AVSR) system is known to have accuracy higher than audio-only or visual-only system. The research focused on the visual front end and has been centered around lip segmentation. Experiments performed for lip feature extraction were mainly done in constrained environment with controlled background noise. In this thesis we focus our attention to a database collected in the environment of a moving car which hampered the quality of the imagery.
We first introduce the concept of illumination compensation, where we try to reduce the dependency of light from over- or under-exposed images. As a precursor to lip segmentation, we focus on a robust face detection technique which reaches an accuracy of 95%. We have detailed and compared three different face detection techniques and found a successful way of concatenating them in order to increase the overall accuracy. One of the detection techniques used was the object detection algorithm proposed by Viola-Jones. We have experimented with different color spaces using the Viola-Jones algorithm and have reached interesting conclusions.
Following face detection we implement a lip localization algorithm based on the vertical gradients of hybrid equations of color. Despite the challenging background and image quality, success rate of 88% was achieved for lip segmentation.
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Westerlund, Tomas. "Fast Face Finding." Thesis, Linköping University, Department of Electrical Engineering, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2068.
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<p>Face detection is a classical application of object detection. There are many practical applications in which face detection is the first step; face recognition, video surveillance, image database management, video coding. </p><p>This report presents the results of an implementation of the AdaBoost algorithm to train a Strong Classifier to be used for face detection. The AdaBoost algorithm is fast and shows a low false detection rate, two characteristics which are important for face detection algorithms. </p><p>The application is an implementation of the AdaBoost algorithm with several command-line executables that support testing of the algorithm. The training and detection algorithms are separated from the rest of the application by a well defined interface to allow reuse as a software library. </p><p>The source code is documented using the JavaDoc-standard, and CppDoc is then used to produce detailed information on classes and relationships in html format. </p><p>The implemented algorithm is found to produce relatively high detection rate and low false alarm rate, considering the badly suited training data used.</p>
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Wall, Helene. "Context-Based Algorithm for Face Detection." Thesis, Linköping University, Department of Science and Technology, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4171.
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<p>Face detection has been a research area for more than ten years. It is a complex problem due to the high variability in faces and amongst faces; therefore it is not possible to extract a general pattern to be used for detection. This is what makes the face detection problem a challenge.</p><p>This thesis gives the reader a background to the face detection problem, where the two main approaches of the problem are described. A face detection algorithm is implemented using a context-based method in combination with an evolving neural network. The algorithm consists of two majors steps: detect possible face areas and within these areas detect faces. This method makes it possible to reduce the search space.</p><p>The performance of the algorithm is evaluated and analysed. There are several parameters that affect the performance; the feature extraction method, the classifier and the images used.</p><p>This work resulted in a face detection algorithm and the performance of the algorithm is evaluated and analysed. The analysis of the problems that occurred has provided a deeper understanding for the complexity of the face detection problem.</p>
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Onder, Murat. "Face Detection And Active Robot Vision." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605290/index.pdf.
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The main task in this thesis is to design a robot vision system with face detection and tracking capability. Hence there are two main works in the thesis: Firstly, the detection of the face on an image that is taken from the camera on the robot must be achieved. Hence this is a serious real time image processing task and time constraints are very important because of this reason. A processing rate of 1 frame/second is tried to be achieved and hence a fast face detection algorithm had to be used. The Eigenface method and the Subspace LDA (Linear Discriminant Analysis) method are implemented, tested and compared for face detection and Eigenface method proposed by Turk and Pentland is decided to be used. The images are first passed through a number of preprocessing algorithms to obtain better performance, like skin detection, histogram equalization etc. After this filtering process the face candidate regions are put through the face detection algorithm to understand whether there is a face or not in the image. Some modifications are applied to the eigenface algorithm to detect the faces better and faster.
Secondly, the robot must move towards the face in the image. This task includes robot motion. The robot to be used for this purpose is a Pioneer 2-DX8 Plus, which is a product of ActivMedia Robotics Inc. and only the interfaces to move the robot have been implemented in the thesis software. The robot is to detect the faces at different distances and arrange its position according to the distance of the human to the robot. Hence a scaling mechanism must be used either in the training images, or in the input image taken from the camera. Because of timing constraint and low camera resolution, a limited number of scaling is applied in the face detection process. With this reason faces of people who are very far or very close to the robot will not be detected. A background independent face detection system is tried to be designed. However the resultant algorithm is slightly dependent to the background. There is no any other constraints in the system.
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Gu, Xiaohan, and Ling Yang. "Face detection based on skin color." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13767.
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This work is on a method for face detection through analysis of photos. Accurate location of faces and point out the faces are implemented. In the first step, we use Cb and Cr channel to find where the skin color parts are on the photo, then remove noise which around the skin parts, finally, use morphology technique to detect face part exactly. Our result shows this approach can detect faces and establish a good technical based for future face recognition.
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Kadoury, Samuel. "Face detection using locally linear embedding." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98976.
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Human face detection in gray scale images has been researched extensively over the past decade, due to the recent emergence of applications such as security access control, visual surveillance and content-based information retrieval. However, this problem remains challenging because faces are non-rigid objects that have a high degree of variability in size, shape, color and texture. Indeed, few of the proposed face detection methods have been analyzed for performance under different conditions, such as head rotation, illumination, facial expression, occlusion and aging.<br>Nowadays, most face detection methods are based upon statistical and learning strategies. Many of these appearance-based methods tend to increase data complexity, by mapping it onto a higher-dimensional space in order to extract the predominant features; this, however, often requires much more computational time. A novel technique that is gaining in popularity, known as Locally Linear Embedding (LLE), adopts a different approach to the problem by applying dimensionality-reduction to the data for learning and classification. Proposed by Roweis and Saul, the objective of this method is to determine a locally-linear fit, so that each data point can be represented by a linear combination of its closest neighbors.<br>The first objective of the current research is to apply the LLE algorithm to 2D facial images, so as to obtain their representation in a sub-space under the unfavorable conditions stated above. The low-dimensional data then will be used to train a Support Vector Machine to classify images as being face or non-face. For this research, six different databases of cropped facial images, corresponding to variations in head rotation, illumination, facial expression, occlusion and aging, were built to train and test the classifiers. The second objective is to evaluate the feasibility of using the combined efficacy of the six SVM classifiers in a two-stage face detection approach. Experimental results obtained with image databases demonstrated that the performance of the proposed method was similar to and sometimes better than other face detection methods, introducing a viable and accurate alternative to previously existing techniques.
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Bileschi, Stanley Michael 1978. "Advances in component-based face detection." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87340.
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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (leaves 51-53).<br>by Stanley Michael Bileschi.<br>S.M.
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Yow, Kin Choong. "Automatic human face detection and localization." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624774.
Full text
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Clemens, Alexander. "Investigating the Inclusivity of Face Detection." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/cmc_theses/1836.
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Face detection refers to a number of techniques that identify faces in images and videos. As part of the senior project exercise at Pomona College, I explore the process of face detection using a JavaScript library called CLMtrackr. CLMtrackr works in any browser and detects faces within the video stream captured by a webcam. The focus of this paper is to explore the shortcomings in the inclusivity of the CLMtrackr library and consequently that of face detection. In my research, I have used two datasets that contain human faces with diverse backgrounds, in order to assess the accuracy of CLMtrackr. The two datasets are the MUCT and PPB. In addition, I investigate whether skin color is a key factor in determining face detection's success, to ascertain where and why a face might not be recognized within an image. While my research and work produced some inconclusive results due to a small sample size and a couple outliers in my outputs, it is clear that there is a trends toward the CLMtrackr algorithm recognizing faces with lighter skin tones more often than darker ones.
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Ribeiro, Ricardo Ferreira. "Face detection on infrared thermal image." Master's thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/23551.
Full textAbstract:
Mestrado em Engenharia Eletrónica e Telecomunicações<br>Infrared cameras or thermal imaging cameras are devices that use infrared
radiation to capture an image. This kind of sensors are being developed for
almost a century now. They started to be used in the military environment,
but at that time it took too long to create a single image. Nowadays, the
infrared sensors have reached a whole new technological level and are used
for other than military purposes. These sensors are being used for face detection
in this thesis.
When comparing the use of thermal images regarding color images, it is
possible to see advantages and limitations, such as capture images in total
darkness and high price, respectively, which will be explored throughout this
document.
This work proposes the development or adaptation of several methods for
face detection on infrared thermal images. The well known algorithm developed
by Paul Viola and Michael Jones, using Haar feature-based cascade
classi ers, is used to compare the traditional algorithms developed for visible
light images when applied to thermal imaging.
Three di erent algorithms for face detection are presented. Face segmentation
is the rst step in these methods. A method for the segmentation
and ltering of the face in the infrared thermal images resulting in a binary
image is proposed. In the rst method, an edge detection algorithm is applied
to the binary image and the face detection is based on these contours.
In the second method, a template matching method is used for searching
and nding the location of a template image with the shape of a human
head in the binary image. In the last one, a matching algorithm is used.
This algorithm correlates a template with the distance transform of the edge
image. This algorithm incorporates edge orientation information resulting
in the reduction of false detection and the cost variation is limited.
The experimental results show that the proposed methods have promising
outcome, but the second method is the most suitable for the performed
experiments.<br>As camaras infravermelhas ou as camaras de imagem termica sao dispositivos
que usam radiação infravermelha para capturar uma imagem. Este
tipo de sensores estao a ser desenvolvidos há quase um século. Começaram
a ser usados para fins militares, mas naquela época demorava demasiado
tempo para criar uma única imagem. Hoje em dia, os sensores infravermelhos
alcançaram um nível tecnológico totalmente novo e são usados para
fins além de militares. Esses sensores estão ser usados para detecção facial
nesta dissertação.
Comparando o uso de imagens térmicas relativamente a imagens coloridas,
é possível ver vantagens e limitações, tal como a captura de imagens na
escuridão e o preço elevado, respectivamente, que serão exploradas durante
este documento.
Este trabalho propõe o desenvolvimento ou adaptação de vários métodos
para a detecção facial em imagens térmicas. O conhecido algoritmo desenvolvido
por Paul Viola e Michael Jones, que utiliza cascatas de classificadores de Haar baseado em características, é usado para comparar os
algoritmos tradicionais desenvolvidos para imagens de luz visível quando
aplicados a imagens térmicas.
São apresentados três métodos diferentes para a detecção facial. A segmentação do rosto e o primeiro passo nestes métodos. E proposto um método para a segmentação e filtragem do rosto nas imagens térmicas que
tem como resultado uma imagem binária. No primeiro método, é aplicado
um algoritmo de detecção de contornos a imagem binária e a detecção facial
é baseada nesses contornos. No segundo método, é usado um método de
correspondência de padrões para pesquisar e encontrar a localização de uma
imagem padrão com a forma da cabeça humana na imagem binária. No
último, é usado um algoritmo de correspondência. Este algoritmo correlaciona
um padrão com a transformada de distância da imagem de contornos.
Este algoritmo incorpora informações de orientação de contornos que resulta
na redução de falsas detecções e a variação do custo é limitada.
Os resultados experimentais mostram que os métodos propostos têm resultados
promissores, mas o segundo método é o mais adequado para as
experiências realizadas.
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Pongakkasira, Kaewmart. "Face detection in complex natural scenes." Thesis, University of Kent, 2015. https://kar.kent.ac.uk/54792/.
Full textAbstract:
Face detection is an important preliminary process for all other tasks with faces, such as expression analysis and person identification. It is also known to be rapid and automatic, which indicates that detection might utilise low-level visual information. It has been suggested that this consist of a ‘skin-coloured, face-shaped template’, while internal facial features, such as the eyes, nose and mouth might also help to optimise performance. To explore these ideas directly, this thesis first examined how shape and features are integrated into a detection template (Chapter 2). For this purpose, face content was isolated into three ranges of spatial frequency, comprising low (LSF), mid (MSF) and high (HSF) frequencies. Detection performance in these conditions was always compared with an original condition, which displayed unfiltered images in the full range of spatial frequency. Across five behavioural and eye-tracking experiments, detection was best for the original condition, followed by MSF, LSF and HSF faces. LSF faces, which provide only crude visual detail (i.e. gross colour shape), were detected as quickly as MSF faces but less accurate. In addition, LSF faces showed a clear advantage over HSF, which contains fine visual information (i.e. detailed lines of the eyes, nose, and mouth), in terms of detection speed and accuracy. These findings indicate that face detection is driven by simple information, such as the saliency of colour and shape, which supports the notion of a skin-coloured faceshape template. However, the fast and more accurate performance for faces in the full and mid-spatial frequencies also indicates that facial features contribute to optimize detection. In Chapter 3, three further eye-tracking experiments are reported, which explore further whether the height-to-width ratio of a coloured-shape template might be important for detection. Performance was best when faces’ natural height-to-width ratios were preserved compared to vertically and horizontally stretched faces. This indicates that this is an important element of the cognitive template for face template. The results also highlight that face detection differs from face recognition, which tolerates the same type of geometric disruption. Based on the results of Chapter 2 and 3, a model of face detection is proposed in Chapter 4. In this model, colour face-shape and features drive detection in parallel, but not necessarily at equal speed, in a “horse race”. Accordingly, rapid detection is normally driven by salient colour and shape cues that preserve the height-to-width ratio of faces, but finer visual detail from features can facilitate this process when further information is needed.
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Liu, Yihui. "Face recognition and face detection based on wavelets and neural networks." Thesis, University of Nottingham, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.403511.
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Boulkenafet, Z. (Zinelabidine). "Face presentation attack detection using texture analysis." Doctoral thesis, Oulun yliopisto, 2018. http://urn.fi/urn:isbn:9789526219257.
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Abstract In the last decades, face recognition systems have evolved a lot in terms of performance. As a result, this technology is now considered as mature and is applied in many real world applications from border control to financial transactions and computer security. Yet, many studies show that these systems suffer from vulnerabilities to spoofing attacks, a weakness that may limit their usage in many cases. A face spoofing attack or presentation attack occurs when someone tries to masquerade as someone else by presenting a fake face in front of the face recognition camera. To protect the recognition systems against attacks of this kind, many face anti-spoofing methods have been proposed. These methods have shown good performances on the existing face anti-spoofing databases. However, their performances degrade drastically under real world variations (e.g., illumination and camera device variations). In this thesis, we concentrate on improving the generalization capabilities of the face anti-spoofing methods with a particular focus on the texture based techniques. In contrast to most existing texture based methods aiming at extracting texture features from gray-scale images, we propose a joint color-texture analysis. First, the face images are converted into different color spaces. Then, the feature histograms computed over each image band are concatenated and used for discriminating between real and fake face images. Our experiments conducted on three color spaces: RGB, HSV and YCbCr show that extracting the texture information from separated luminance chrominance color spaces (HSV and YCbCr) yields to better performances compared to gray-scale and RGB image representations. Moreover, to deal with the problem of illumination and image-resolution variations, we propose to extract this texture information from different scale images. In addition to representing the face images in different scales, the multi-scale filtering methods also act as pre-processing against factors such as noise and illumination. Although our obtained results are better than the state of the art, they are still far from the requirements of real world applications. Thus, to help in the development of robust face anti-spoofing methods, we collected a new challenging face anti-spoofing database using six camera devices in three different illumination and environmental conditions. Furthermore, we have organized a competition on the collected database where fourteen face anti-spoofing methods have been assessed and compared<br>Tiivistelmä Kasvontunnistusjärjestelmien suorituskyky on parantunut huomattavasti viime vuosina. Tästä syystä tätä teknologiaa pidetään nykyisin riittävän kypsänä ja käytetään jo useissa käytännön sovelluksissa kuten rajatarkastuksissa, rahansiirroissa ja tietoturvasovelluksissa. Monissa tutkimuksissa on kuitenkin havaittu, että nämä järjestelmät ovat myös haavoittuvia huijausyrityksille, joissa joku yrittää esiintyä jonakin toisena henkilönä esittämällä kameralle jäljennöksen kohdehenkilön kasvoista. Tämä haavoittuvuus rajoittaa kasvontunnistuksen laajempaa käyttöä monissa sovelluksissa. Tunnistusjärjestelmien turvaamiseksi on kehitetty lukuisia menetelmiä tällaisten hyökkäysten torjumiseksi. Nämä menetelmät ovat toimineet hyvin tätä tarkoitusta varten kehitetyillä kasvotietokannoilla, mutta niiden suorituskyky huononee dramaattisesti todellisissa käytännön olosuhteissa, esim. valaistuksen ja käytetyn kuvantamistekniikan variaatioista johtuen. Tässä työssä yritämme parantaa kasvontunnistuksen huijauksen estomenetelmien yleistämiskykyä keskittyen erityisesti tekstuuripohjaisiin menetelmiin. Toisin kuin useimmat olemassa olevat tekstuuripohjaiset menetelmät, joissa tekstuuripiirteitä irrotetaan harmaasävykuvista, ehdotamme väritekstuurianalyysiin pohjautuvaa ratkaisua. Ensin kasvokuvat muutetaan erilaisiin väriavaruuksiin. Sen jälkeen kuvan jokaiselta kanavalta erikseen lasketut piirrehistogrammit yhdistetään ja käytetään erottamaan aidot ja väärät kasvokuvat toisistaan. Kolmeen eri väriavaruuteen, RGB, HSV ja YCbCr, perustuvat testimme osoittavat, että tekstuuri-informaation irrottaminen HSV- ja YCbCr-väriavaruuksien erillisistä luminanssi- ja krominanssikuvista parantaa suorituskykyä kuvien harmaasävy- ja RGB-esitystapoihin verrattuna. Valaistuksen ja kuvaresoluution variaation takia ehdotamme myös tämän tekstuuri-informaation irrottamista eri tavoin skaalatuista kuvista. Sen lisäksi, että itse kasvot esitetään eri skaaloissa, useaan skaalaan perustuvat suodatusmenetelmät toimivat myös esikäsittelynä sellaisia suorituskykyä heikentäviä tekijöitä vastaan kuten kohina ja valaistus. Vaikka tässä tutkimuksessa saavutetut tulokset ovat parempia kuin uusinta tekniikkaa edustavat tulokset, ne ovat kuitenkin vielä riittämättömiä reaalimaailman sovelluksissa tarvittavaan suorituskykyyn. Sen takia edistääksemme uusien robustien kasvontunnistuksen huijaamisen ilmaisumenetelmien kehittämistä kokosimme uuden, haasteellisen huijauksenestotietokannan käyttäen kuutta kameraa kolmessa erilaisessa valaistus- ja ympäristöolosuhteessa. Järjestimme keräämällämme tietokannalla myös kansainvälisen kilpailun, jossa arvioitiin ja verrattiin neljäätoista kasvontunnistuksen huijaamisen ilmaisumenetelmää
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Day, Matthew. "Towards optimised cascade classifiers for face detection." Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.495869.
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Omar, Luma Qassam Abedalqader. "Face liveness detection under processed image attacks." Thesis, Durham University, 2018. http://etheses.dur.ac.uk/12812/.
Full textAbstract:
Face recognition is a mature and reliable technology for identifying people. Due to high-definition cameras and supporting devices, it is considered the fastest and the least intrusive biometric recognition modality. Nevertheless, effective spoofing attempts on face recognition systems were found to be possible. As a result, various anti-spoofing algorithms were developed to counteract these attacks. They are commonly referred in the literature a liveness detection tests. In this research we highlight the effectiveness of some simple, direct spoofing attacks, and test one of the current robust liveness detection algorithms, i.e. the logistic regression based face liveness detection from a single image, proposed by the Tan et al. in 2010, against malicious attacks using processed imposter images. In particular, we study experimentally the effect of common image processing operations such as sharpening and smoothing, as well as corruption with salt and pepper noise, on the face liveness detection algorithm, and we find that it is especially vulnerable against spoofing attempts using processed imposter images. We design and present a new facial database, the Durham Face Database, which is the first, to the best of our knowledge, to have client, imposter as well as processed imposter images. Finally, we evaluate our claim on the effectiveness of proposed imposter image attacks using transfer learning on Convolutional Neural Networks. We verify that such attacks are more difficult to detect even when using high-end, expensive machine learning techniques.
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Zhou, Yun. "Embedded Face Detection and Facial Expression Recognition." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/583.
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Face Detection has been applied in many fields such as surveillance, human machine interaction, entertainment and health care. Two main reasons for extensive attention on this typical research domain are: 1) a strong need for the face recognition system is obvious due to the widespread use of security, 2) face recognition is more user friendly and faster since it almost requests the users to do nothing. The system is based on ARM Cortex-A8 development board, including transplantation of Linux operating system, the development of drivers, detecting face by using face class Haar feature and Viola-Jones algorithm. In the paper, the face Detection system uses the AdaBoost algorithm to detect human face from the frame captured by the camera. The paper introduces the pros and cons between several popular images processing algorithm. Facial expression recognition system involves face detection and emotion feature interpretation, which consists of offline training and online test part. Active shape model (ASM) for facial feature node detection, optical flow for face tracking, support vector machine (SVM) for classification is applied in this research.
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Norris, Jeffrey S. (Jeffrey Singley) 1976. "Face detection and recognition in office environments." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80108.
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Fraga, António Fernando Crisóstomo. "Parallel Face Detection." Master's thesis, 2020. http://hdl.handle.net/10316/94026.
Full textAbstract:
Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia<br>O reconhecimento de faces em imagens é atualmente feito em grande escala e as imagens utilizadas tende a ser cada vez mais de resolução mais elevadas. Isto pode ser um desafio complicado em arquiteturas sequenciais, pois, com o aumento do número total de pixels das imagens, o desempenho geral desse tipo de implementações tende a diminuir drasticamente. A tese apresentada descreve a implementação de uma framework baseada no artigo Viola-Jones “Rapid Object Detection using a Boosted Cascade of Simple Features” [2]. Desta forma, as arquiteturas paralelas (GPUs e GPUs de baixo consumo), emergem como a solução ideal já que oferecem elevados valores de poder computacional e números de cores que beneficiam o processamento de grandes quantidades de data em paralelo. Utilizando, assim, as vantagens destas arquiteturas para uma paralelização e otimização específica a esta implementação, obtendo, portanto, uma melhoria significativa na performance em comparação a arquiteturas sequenciais em imagens de alta resolução. Por sua vez, também é realizada uma análise dos resultados desta implementação, que acaba por ser bem-sucedida em diversas GPUs, com o objetivo de fazer uma análise conclusiva da influência dos recursos de GPU disponíveis (Power, CUDA cores, etc.) na aceleração geral da GPU. De referir ainda que este detetor de caras baseado em arquiteturas paralelas foi capaz de obter uma aceleração global de até 33 vezes superior em imagens de 8k em comparação com a versão sequencial inicialmente implementada.<br>Face detection is typically used millions of times per day in many different contexts and the resolution of the images has seen a significant increase. These high-resolution images can be a very defiant challenge in sequentially based architecture since with the rise in the number of pixels the overall performance of this type of implementation decreases drastically.The following paper describes the implementation of a framework of the Viola-Jones “Rapid Object Detection using a Boosted Cascade of Simple Features” [2] in parallel architectures such as GPUs and low-power GPUs. They emerge as natural candidates for the acceleration that we seek, offering a very high computational power and core numbers that enable the process of such large amounts of data in parallelIt also shows the parallelization and optimization of the implementation utilizing the advantages offered by these architectures to achieve an overall performance boost and speedup in high-resolution images when comparing to sequential architectures. An analysis of the results shows the successful implementation and the influence that the GPU resources available (Power, CUDA cores, etc.) have on the overall GPU speedup as well as in its performance. This parallel face detector implementation was able to obtain a global speedup as high as 33 times in 8k images in comparison with the sequential version. An analysis of the results shows the successful implementation and the influence that the GPU resources available (Power, CUDA cores, etc.) have on the overall GPU speedup as well as in its performance. This parallel face detector implementation was able to obtain a global speedup as high as 33 times in 8k images in comparison with the sequential version.
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Sahu, Sameer, and Pappu Thakur. "Human Face Detection." Thesis, 2012. http://ethesis.nitrkl.ac.in/3730/1/Human_face_detection.pdf.
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Face Detection is fast becoming a familiar feature in various technical fields such as video surveillance, military applications, apps and on web, potentially making life easier for us. In current era of social networking Face Detection is a hot topic of research in both academics and commercial area throughout the world. Quite interestingly, the variation in the human skin colour between different races existing in this world primarily is the intensity that is proportional to the amount of melanin in the skin. So our approach towards face detection which uses skin colour seems effective since the skin colour database for various races can easily be collected. In our project, we have studied and worked on face detection techniques and developed an algorithm that detects human faces in an image. Our algorithm identifies possible skin regions in an image and using the skin colour spread in the whole image detects faces. We have taken our own still images as examples and simulated the algorithm in MATLAB 7.10.0 successfully.
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Tsai, Tung-Sheng, and 蔡東昇. "Implementation of Face Detection and Face Tracking." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/91569976255420901425.
Full textAbstract:
碩士<br>大葉大學<br>資訊工程學系碩士班<br>100<br>In the thesis, we implemented a face detection and tracking system. The developed system is composed of two main parts: face detection and face tracking.
In the face detection part, a face detector using Haar-Like features trained by Adaboost algorithm is adopted to detect facial region. To remove the error of face region, the human eyes information can also be used. After the face detection was completed, each face candidate can be tracked in the temporal domain. In the face tracking part, KLT features are extracted and tracked between two adjacent frames. Based on KLT feature tracking, face tracking can be achieved in the developed system.
To evaluate the developed system, several videos with different kinds of face movement are captured by using low-cost webcam. Experimental results show that our proposed system can detect and track facial regions well. The detection rate of our face detection is more than 96% and the detection rate of our face tracking is more than 91%. These results demonstrate that our proposed system can achieve face detection and face tracking in real-world noisy videos.
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chao, chang min, and 張閔詔. "Covered Face Detection and RecognitionCovered Face Detection and Recognition using Decision Tree." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/05130351792301190914.
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謝昌甫. "Human face detection system." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/33813411288787563185.
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碩士<br>中華大學<br>資訊工程學系碩士班<br>89<br>With the improvement of science and technology, the usage of human-machine interface is more popular and convenient in the recent years. A fast and accu-rate face detection approach is important for several applications, such as face recognition, security system, human-machine interface and TV-conference,etc.
This thesis proposes a new method to detect human eyes and uses feature-based approach with fuzzy logic theorem for face candidates confirmation. According to this detecting scheme, we can obtain the face location, face tilt angle, face size and predict the distance between a human and the camera.
These information are very useful for further applications.
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Ferreira, José Duarte Penetro Mesquita. "Cross-Sensor Face Detection." Master's thesis, 2021. https://hdl.handle.net/10216/135359.
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Ferreira, José Duarte Penetro Mesquita. "Cross-Sensor Face Detection." Dissertação, 2021. https://hdl.handle.net/10216/135359.
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Tuyen, Kha Kim, and 柯金泉. "Face Detection with RetinaNet." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/y7r4c6.
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碩士<br>國立中央大學<br>資訊工程學系在職專班<br>107<br>Abstract
Face detection is a critical step for many face-related applications, such as face alignment, face verification, face identification, crowed behavior analysis etc. However, small size, occlusion, illumination, pose deformation, expression and other disadvantageous factors often appear in real-world images, which bring great challenges to face detection. Besides, computation cost is also a big challenge for face detection in real-time application.
Traditional approach use manual operation with slide windows to skim and detect face location, it cost much computation and affect accuracy, especially with small size face. Recently, generic object detection based on deep convolution neural networks (CNNs) has achieved great success. It utilizes modern object detectors including one stage methods (e.g., YOLO, SSD) and two stage methods (e.g., Faster RCNN, RFCN). One stage methods refer broadly to architectures that use a single feed-forward full convolutional neural network to directly predict each proposal’s class and corresponding bounding box without requiring a second stage per-proposal classification operation and box refinement . Therefore, one stage methods success in computation cost whereas two stage mothods winner accuracy performance.
In this research, I deployed RetiaNet for face detection, it could solve the small size problem as well as computation cost; especially, it has benefit of both one-stage and two-stage methods .
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李宗岳. "Dynamic Face Detection via Adaptive Face Features Extraction." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/07139451006948106318.
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Ren-Shan, Luoh, and 駱仁山. "A Study of Face Detection for Face Recognition." Thesis, 1999. http://ndltd.ncl.edu.tw/handle/55999885264256830205.
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碩士<br>國立交通大學<br>資訊工程系<br>87<br>We propose a method for detecting human faces in a color picture.
By applying a skin color model,
we label some rectangular regions in the picture as skin.
After merging several small skin regions and sifting the proper ones,
we can quickly locate the possible face images in a picture.
Since the skin regions are relative smaller than the original picture image,
the overall computing time can be greatly reduced.
In order to precisely locate the eye in the face image,
we use some criteria such as the distance between two eyes,
the intensity of the neighborhood and a circular pattern to locate both eyes.
Finally one can use the coordinates of both eyes to properly
extract the facial features for face recognition and/or some other applications.
We also perform some experiments on the proposed method.
Our face database contains 755 images taken from 151 persons.
Each person has five photos taken from five different angles
($0^{\circ}, \pm22.5^{\circ},\pm45^{\circ}$)
The correction rate is 67.55\% with fault toleration smaller than 3 pixels,
and 70.20\% with fault toleration smaller than 5 pixels.
The average overall running time is 0.83 second on a AMD-K6-2 300 machine.
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Chen, Chihhao, and 陳致豪. "Speedup AdaBoost Face Detection by Skin Color Detection." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/44807696820261382608.
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碩士<br>明志科技大學<br>電子工程研究所<br>99<br>Study on human face detection is originated from human face recognition, and it aims to recognize human face in images and identify the position of human face. In modern time, with the prevalence of imaging system and the rise of computer vision, researches on human face detection develop rapidly. This study proposes a human face detection method based on dynamic images. The paper is organized into four parts. 1) It first implements the human face detection proposed by Viola upon AdaBoost, and analyze efficacy by cascade provided by OpenCV. 2) It then develops two-stage human face detection for dynamic image by detection of the color of skin. After reducing the dimension of searching area, it can reduce detection, and avoid the errors. 3) It proposes the detection to replace the whole face characteristics by eyes. Experimental result showed that although actual detection rate will reduce by 15%, detection speed can increase by 3 times. 4) Experiments were conducted on complicated background, races of different colors of skin and multiple postures of human face, and tested the common issues of photography, such as detection distance, opacity, brightness and contrast. The experimental results confirmed that the system is stable human face detection.
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Hu, Shyuegong, and 胡學恭. "Multiple-Face Detection & Face Recognition for Complex Backgrounds." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/87643401522123211970.
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碩士<br>元智大學<br>資訊管理學系<br>98<br>Human face detection and human face recognition are always popular topics in pattern recognition, they have develop for a long time, and its technique has become more and more robust. At the past, some technique could be restricted by hardware or other experimental settings, so they should made some kind of trade off between detection rate, recognition rate and computing time. Now days, with the development of technology and other equipment some time consuming methods become available.
We present a human face detection method which is base on haar-like features and integral image and a human face recognition method which divide human face into different feature area, then use machine learning to divide the feature groups and classify them by these feature information.
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Serre, Thomas, Bernd Heisele, Sayan Mukherjee, and Tomaso Poggio. "Feature Selection for Face Detection." 2000. http://hdl.handle.net/1721.1/7232.
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We present a new method to select features for a face detection system using Support Vector Machines (SVMs). In the first step we reduce the dimensionality of the input space by projecting the data into a subset of eigenvectors. The dimension of the subset is determined by a classification criterion based on minimizing a bound on the expected error probability of an SVM. In the second step we select features from the SVM feature space by removing those that have low contributions to the decision function of the SVM.
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Mei-Heng, Lin, and 林美亨. "Face Detection in Scalable Video." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/03355681428582322876.
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碩士<br>國立交通大學<br>資訊學院碩士在職專班資訊組<br>95<br>3D wavelet scalable video coding has the advantages of bandwith, temporal, and spatial scalability, which includes three main modules : T_Module、S_Module、Entropy_Coding, those produce final compressed output bit stream.
In this thesis we would like to combine face detection in 3D wavelet scalable video. The advantages are: 1. User only download one bit stream and extract it for what they need. 2. Server only provides one bit stream for all users to download and it also saves lots of disk space in network.
We propose a strategy that makes the face can be detected under different environment and detect the face correctly and quickly.
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Effendi, Mohammad Khoirul, and 仁迪芬. "Cascade AdaBoost for Face Detection." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/15976892970662808799.
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碩士<br>國立臺灣科技大學<br>機械工程系<br>98<br>AdaBoost is a well developed method for classification, and has been applied to face detection. Given a large set of training data, AdaBoost-based face detector combines many weak classifiers, each of which is targeted at a set of features with different characteristics, and generates a strong classifier able to detect faces with large variations on the appearances. However, when the patterns embedded in the positive and negative training data conflict to each other, the training can often become time-consuming, and in some cases, be extremely difficult to converge. This research proposes a cascade architecture for AdaBoost, known as Cascade AdaBoost, which decomposes the training data into clusters with similar features. A component face detector is obtained using AdaBoost classification on each cluster of data, and the combination of all component face detectors contributes to the overall face detector. This research also studies the impacts of features with gray-scale pixel values and pixels with skin colors. Experiments on PIE and FRGC databases reveal that the proposed Cascade AdaBoost face detector can converge much faster than the conventional AdaBoost method, and that the skin color can substantially improve the accuracy and speed of face detection.
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李俊達. "Face detection in Color Image." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/06137633306875184263.
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碩士<br>國立政治大學<br>資訊科學學系<br>95<br>The main goal of this thesis is to detect human face under varying lighting condition by utilizing multiple color space information in real-time. Images of RGB color space can be converted into normalized RGB and HSV color spaces and thus reduce the interference of lighting condition. Base on this mechanism, we define 8 Haar-like features inside 4 selected color spaces, and then select the important features with boosting algorithm. Experimental results show that detectors constructed with our approach are able to process nearly one million sub-windows within 2.4 seconds, being robust to the changes of lighting conditions.
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Jhang, Yuan-Ruei, and 張元睿. "Face Detection in Still Image." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/78355345614201711713.
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碩士<br>國立臺灣大學<br>醫學工程學研究所<br>92<br>Within the automatic human face identification system, a good human face detecting system is needed to serve as a prerequisite, yet within a standstill grey scale image, there is only an intensity variation value on each image element, which lacks information on colored and sequential image, therefore, to be able to accurately circle out the size of the human face within the image and its position only increases its difficulty.
Currently majority of the literary review only utilized image’s grey scale value on the mechanical learning or statistic analysis to conduct human face detection, though this method has pretty good detecting ratio, nevertheless, in the area of its classifier design, it can be more complicated. This thesis has proposed a similar template matching method that has produced a feature vector, and then utilized a simpler statistic analysis to conduct sorting on human face and non-human face pattern, the experimental result has a very good detecting ratio; moreover, this feature concept can be applied on detecting other objects or on its identification.
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Shen, Chi-Liang, and 沈啟亮. "Real Time Face Detection System." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/51621379084753012144.
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碩士<br>淡江大學<br>電機工程學系碩士在職專班<br>92<br>Real-time face detection and face recognition from cluttered images is a challenging work. This dissertation proposes a hierarchical window based on the facial features as an operating mask for human eyes detection so as to be adaptable for different face sizes in images. After the input image is processed using the Sobel filter edge detection, we first make use of the characteristics of symmetric relationships of human-eyes region to find out the possible region of human eyes. Then, to exclude the region of without-human eyes and quickly reach the locations of possible face region, the author capitalizes the geometric characteristic of human-eyes region. Finally, after the eyes verification we accomplish the real-time face detection. The dissertation adopts video card and CCD as live video images for face detection system. It carries out on the computer of PC (Pentium 4 1.8 G). From the website, the author establishes the Color database (WCset) which amounts to 328 pieces of color images. On the average takes only 0.05 seconds for each color image (256*384*1.6M) and the accuracy is 90%(296/328). Again from the faces of FERET (384*256*256) the author establishes the gray-level image database Which contains 2164 pieces. Each image takes about 0.02 seconds and the accurate rate is 95.7 % (2071/2164). The performance is better than the other current face detection systems.
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Sun, Shih-Ching, and 孫世清. "Real Time Face Detection Algorithm." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/24896181450898652075.
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碩士<br>國立東華大學<br>電機工程學系<br>91<br>In recent years, human face detection is becoming more and more popular. Automatically detecting human faces is becoming a very important task in various applications such as video surveillance, human computer interface, face recognition and face image database management. In the face recognition application, the human face location must be known before the processing. The face tracking application also needs a predefined face location at first. In the face image database management, the human faces must be discovered as fast as possible due to the large image database. Although numerous methods are currently used to perform the face detection, there are still many factors that make the face detection more difficult, such as scale, location, orientation, occlusion, expression and wearing glasses. Various approaches of face detection are proposed in recent years, but rare of them take all of the factors above into account. However, a face detection technique that can be used in any real time application needs to satisfy the factors above.
In this thesis, we propose a novel method to deal with the above difficulties. The objective is to detect the face region for video sequences. Therefore, the face pose should not be laminated. We propose a fast algorithm of face detection based on color, motion and facial feature analysis. Firstly, we use a set of chrominance values to obtain the skin color region. Secondly, we propose a novel method for segmenting the motion region by the enhanced frame difference. Then, we combine the skin color region and the motion region to locate the face candidates. We propose a robust eye detection method to detect the eyes in the detected face candidates region. Finally, we verify each eye pair to decide the validity of the face candidate.
According to the experiment results, the user need not be restricted when detecting the face. In general condition, the user could have wide range of face activity such as different position, size, orientation, view and facial expression. Besides, the proposed algorithm also has a satisfied detection rate even if the user is wearing glasses. The detection speed can achieve 30 frames per second for CIF sequence and 120 frames per second for QCIF sequence. Consequently, the proposed method is robust, practical and efficient.
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Chou, Heng-Shen, and 周恆生. "Face Detection in Color Images." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/88891165686958406869.
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碩士<br>國立清華大學<br>資訊工程學系<br>91<br>Due to the rapid growth of technologies for network and multimedia, digital input devices have become more and more popular, and the cost of the digital image is much lower now. Various applications which using facial features like personal identify and facial expression capture are coming to the mature period. However, face detection plays an important role in these issues. A good face detection algorithm could slash the computational time in verifying probable face candidates, and improve the comparing efficiency.
In this paper, we propose a face detection algorithm. In our method, after smoothed the picture to reduce thin color region effects, we can decide which regions are in the skin-tone scope by the color threshold, Morphology operations help us to exclude the parts which not fit in the right size and proportion. Then we divide the probable face candidate into three parts, and try to find the major objects in each divided area. Consider their color characteristic and position relations; we can get the correct face region.
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Lee, Tsung-Pei, and 李宗霈. "Face Detection on Embedded Systems." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/99454969346388372578.
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碩士<br>國立臺北教育大學<br>資訊科學系碩士班<br>103<br>Vehicle inventions facilitate human life but also pose a threat to human lives. The slightest mistake while operating them frequently caused heavy casualties and loss of life. Thus, traffic safety issues have the wide attention of governments.
Biometrics can automatically identify the individual physiological or behavioral aspect of the system. Different conditions can be set according to different needs in order to achieve the desired purpose. Face detection is the basis for automation systems. Face application needs to have strong face detection methods in order to achieve the desired results. The architecture of the face detection method substantially consists of the following project components: determine positions of the candidate’s face; capture and detect the positions of facial features of the human face. In this paper, two-dimensional face recognition, combined with an embedded camera system, attempts to understand through face detection the behavior of motorists while driving and strengthen research-related systems used in the feasibility of road safety.
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Garcia, Jorge Renato Torres. "Face detection from video streaming." Master's thesis, 2018. https://hdl.handle.net/10216/112424.
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Chang, Yen-An, and 張宴安. "Human Face Detection and Application." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/32868867088619962367.
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碩士<br>淡江大學<br>電機工程學系碩士班<br>95<br>Recently, the Biosensor system has been mostly applied to the security of the community or the company. Despite the efficiency of the identification of the human’s figure, this system isn’t applied extensively in our society. The aim of this thesis is to propose a sound human-face detection and recognition system that is different from the traditional one, which can only tell whether the figure passes the detection or not without taking any further measurement. The system proposed here can find out whether the figure is masked or not and then proceed to recognize the face. With this new function, the system can be more efficient by finding out why the recognition fails and whether the visitor is a suspect or not. Then the security guard can take further action immediately to ensure the security.
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Kuo, Tsung-Yu, and 郭宗祐. "Face Detection Using Geometrical Information." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/58216973606685499337.
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碩士<br>義守大學<br>資訊工程學系碩士班<br>94<br>This thesis aims to the study of face detection system. First, histogram of image intensity is computed from an image. According to the peak value of the histogram, we can determine a threshold and then find the pixels that could be eye candidates. These pixels are merged to eye candidates that are represented by a binary image. To identify which one is a real eye, we examine each of its area and remove the impossible ones. Finally, we compute the convolution integral of each candidate and a simple mask that is designed based on geometrical information of human face. Experimental results indicate that the proposed method can effectively detect human eyes and achieves fast computation.