Relevant bibliographies by topics / Face detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Face detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 June 2025

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Journal articles on the topic "Face detection"

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Hajiarbabi, Mohammadreza, and Arvin Agah. "Techniques for Skin, Face, Eye and Lip Detection using Skin Segmentation in Color Images." International Journal of Computer Vision and Image Processing 5, no. 2 (2015): 35–57. http://dx.doi.org/10.4018/ijcvip.2015070103.

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Face detection is a challenging and important problem in Computer Vision. In most of the face recognition systems, face detection is used in order to locate the faces in the images. There are different methods for detecting faces in images. One of these methods is to try to find faces in the part of the image that contains human skin. This can be done by using the information of human skin color. Skin detection can be challenging due to factors such as the differences in illumination, different cameras, ranges of skin colors due to different ethnicities, and other variations. Neural networks have been used for detecting human skin. Different methods have been applied to neural networks in order to increase the detection rate of the human skin. The resulting image is then used in the detection phase. The resulting image consists of several components and in the face detection phase, the faces are found by just searching those components. If the components consist of just faces, then the faces can be detected using correlation. Eye and lip detections have also been investigated using different methods, using information from different color spaces. The speed of face detection methods using color images is compared with other face detection methods.
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Liu, Linrunjia, Gaoshuai Wang, and Qiguang Miao. "ADYOLOv5-Face: An Enhanced YOLO-Based Face Detector for Small Target Faces." Electronics 13, no. 21 (2024): 4184. http://dx.doi.org/10.3390/electronics13214184.

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Benefiting from advancements in generic object detectors, significant progress has been achieved in the field of face detection. Among these algorithms, the You Only Look Once (YOLO) series plays an important role due to its low training computation cost. However, we have observed that face detectors based on lightweight YOLO models struggle with accurately detecting small faces. This is because they preserve more semantic information for large faces while compromising the detailed information for small faces. To address this issue, this study makes two contributions to enhance detection performance, particularly for small faces: (1) modifying the neck part of the architecture by integrating a Gather-and-Distribute mechanism instead of the traditional Feature Pyramid Network to tackle the information fusion challenges inherent in YOLO-based models; and (2) incorporating an additional detection head specifically designed for detecting small faces. To evaluate the performance of the proposed face detector, we introduce a new dataset named XD-Face for the face detection task. In the experimental section, the proposed model is trained using the Wider Face dataset and evaluated on both Wider Face and XD-face datasets. Experimental results demonstrate that the proposed face detector outperforms other excellent face detectors across all datasets involving small faces and achieved improvements of 1.1%, 1.09%, and 1.35% in the AP50 metric on the WiderFace validation dataset compared to the baseline YOLOv5s-based face detector.
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Anjali, Muneshwar, and Vattam Prof.Jayarajesh. "Face Detection System with Face Recognition." International Organization of Research & Development (IORD) 9, no. 1 (2021): 5. https://doi.org/10.5281/zenodo.5016190.

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The face is one of the easiest ways to distinguish the individual identity of each other. Face recognition is a personal identification system that uses the personal characteristics of a person to identify the person's identity. The human face recognition procedure basically consists of two phases, namely face detection, where this process takes place very rapidly in humans, except under conditions where the object is located at a short distance away, the next is the introduction, which recognizes a face as individuals. The stage is then replicated and developed as a model for facial image recognition (face recognition) is one of the much-studied biometrics technology and developed by experts. There are two kinds of methods that are currently popular in developed face recognition patterns, namely, the Eigenface method and the Fisherface method. Facial image recognition Eigenface method is based on the reduction of face dimensional space using Principal Component Analysis (PCA) for facial features. The main purpose of the use of PCA on face recognition using Eigenfaces was formed (face space) by finding the eigenvector corresponding to the largest eigenvalue of the face image. The area of this project's face detection system with face recognition is Image processing. The software required for this project is Matlab software.
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Nam, Amir Nobahar Sadeghi. "Face Detection." Volume 5 - 2020, Issue 9 - September 5, no. 9 (2020): 688–92. http://dx.doi.org/10.38124/ijisrt20sep391.

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Face detection is one of the challenging problems in the image processing, as a main part of automatic face recognition. Employing the color and image segmentation procedures, a simple and effective algorithm is presented to detect human faces on the input image. To evaluate the performance, the results of the proposed methodology is compared with ViolaJones face detection method.
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Lewis, Michael B., and Andrew J. Edmonds. "Face Detection: Mapping Human Performance." Perception 32, no. 8 (2003): 903–20. http://dx.doi.org/10.1068/p5007.

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The recognition of faces has been the focus of an extensive body of research, whereas the preliminary and prerequisite task of detecting a face has received limited attention from psychologists. Four experiments are reported that address the question how we detect a face. Experiment 1 reveals that we use information from the scene to aid detection. In experiment 2 we investigated which features of a face speed the detection of faces. Experiment 3 revealed inversion effects and an interaction between the effects of blurring and reduction of contrast. In experiment 4 the sizes of effects of reversal of orientation, luminance, and hue were compared. Luminance was found to have the greatest effect on reaction time to detect faces. The results are interpreted as suggesting that face detection proceeds by a pre-attentive stage that identifies possible face regions, which is followed by a focused-attention stage that employs a deformable template. Comparisons are drawn with automatic face-detection systems.
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Omaima, N. A. AL-Allaf. "Review of Face Detection Systems Based Artificial Neural Networks Algorithms." International Journal of Multimedia & Its Applications (IJMA) 6, no. 1 (2021): 1–16. https://doi.org/10.5281/zenodo.4730130.

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Face detection is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. There is lack of literature surveys which give overview about the studies and researches related to the using of ANN in face detection. Therefore, this research includes a general review of face detection studies and systems which based on different ANN approaches and algorithms. The strengths and limitations of these literature studies and systems were included also.
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Nidhi, Soni* Priya Mate. "FACE DETECTION AND RECOGNIZATION USING PCA ALGORITHM." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 5 (2017): 717–21. https://doi.org/10.5281/zenodo.801247.

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Image databases and live video data is growing rapidly, their intelligent or automatic examining is becoming exceptionally more important. Human faces are one of very common and very particular objects that we need to try to detect in images. Face detection is very difficult task in image analysis which has each day many applications. We can illustrate the face detection problem as a computer vision task which involve in detecting one or several human faces in an image. Identification & Authentication has become major problems in present digital world. Face detection plays a significant role in identification & authentication. In this paper we propose mechanism for detecting faces from low luminance video and poor quality video files using PCA and Voila Jones Algorithm. We show effectiveness of our algorithm by taking low light video and poor quality video for comparison.
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Nidhi, Soni, and Mate2 Priya. "FACE DETECTION AND RECOGNIZATION USING PCA ALGORITHM." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 4, no. 6 (2017): 21–25. https://doi.org/10.5281/zenodo.802177.

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Image databases and live video data is growing rapidly, their intelligent or automatic examining is becoming exceptionally more important. Human faces are one of very common and very particular objects that we need to try to detect in images. Face detection is very difficult task in image analysis which has each day many applications. We can illustrate the face detection problem as a computer vision task which involve in detecting one or several human faces in an image. Identification & Authentication has become major problems in present digital world. Face detection plays a significant role in identification & authentication. In this paper we propose mechanism for detecting faces from low luminance video and poor quality video files using PCA and Voila Jones Algorithm. We show effectiveness of our algorithm by taking low light video and poor quality video for comparison
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E, Subash, Hariprasath M, Aathithya S, et al. "Attendance Management System Using Face Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43195.

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This paper presents a Hybrid Multi-Stage Face Detection Algorithm that integrates traditional and deep learning methods for improved accuracy and efficiency. The process begins with Preprocessing and Enhancement to refine image quality. Fast Face Candidate Selection (Haar + HOG + SVM) quickly detects potential faces, followed by Precise Localization using MTCNN to refine detections and extract facial landmarks. Deep Learning Verification (RetinaFace/YOLO) eliminates false positives, ensuring reliability. Finally, Face Tracking (Kalman Filter + SORT) maintains consistency in video streams. This approach provides a robust and adaptable solution for real-world face detection applications. Keywords : Face Detection, Hybrid Algorithm, Deep Learning, Haar Cascade, HOG + SVM, MTCNN, RentinaFace, YOLO, Face Tracking, Real-Time Processing
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Hayashi, Shinji, and Osamu Hasegawa. "Robust Face Detection for Low-Resolution Images." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 1 (2006): 93–101. http://dx.doi.org/10.20965/jaciii.2006.p0093.

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Face detection, one of the most actively researched and progressive computer vision fields, has been little studied in low-resolution images. Using the AdaBoost-based face detector and MIT+CMU frontal face test set – the standard detector and images for evaluation in face detection – we found that face detection rate falls to 39% from 88% as face resolution decreases from 24×24 pixels to 6×6 pixels. We discuss a proposal using “portrait images,” “image expansion,” “frequency-band limitation of features” and “two-detector integration” and show that 71% of face detection rate is obtained for 6×6 pixel faces of MIT+CMU frontal face test set. Note that each of the above detections involves 100 false positives for 112 evaluation images.
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Dissertations / Theses on the topic "Face detection"

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Espinosa-Romero, Arturo. "Situated face detection." Thesis, University of Edinburgh, 2001. http://hdl.handle.net/1842/6667.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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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Books on the topic "Face detection"

1

Zhang, Cha. Boosting-Based Face Detection and Adaptation. Springer International Publishing, 2010. http://dx.doi.org/10.1007/978-3-031-01809-1.

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. Multi-Modal Face Presentation Attack Detection. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-031-01824-4.

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Wan, Jun, Guodong Guo, Sergio Escalera, Hugo Jair Escalante, and Stan Z. Li. Advances in Face Presentation Attack Detection. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-32906-7.

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1965-, Zhang Zhengyou, ed. Boosting-based face detection and adaptation. Morgan & Claypool, 2010.

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Rathgeb, Christian, Ruben Tolosana, Ruben Vera-Rodriguez, and Christoph Busch, eds. Handbook of Digital Face Manipulation and Detection. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-87664-7.

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Kawulok, Michal, M. Emre Celebi, and Bogdan Smolka, eds. Advances in Face Detection and Facial Image Analysis. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25958-1.

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Yang, Ming-Hsuan, and Narendra Ahuja. Face Detection and Gesture Recognition for Human-Computer Interaction. Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1423-7.

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McCready, Robert. Real-time face detection on a configurable hardware platform. National Library of Canada, 2000.

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Yang, Ming-Hsuan. Face Detection and Gesture Recognition for Human-Computer Interaction. Springer US, 2001.

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1950-, Ahuja Narendra, ed. Face detection and gesture recognition for human-computer interaction. Kluwer Academic, 2001.

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Book chapters on the topic "Face detection"

1

Yu, Shiqi, Yuantao Feng, Hanyang Peng, Yan-ran Li, and Jianguo Zhang. "Face Detection." In Handbook of Face Recognition. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-43567-6_4.

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Loy, Chen Change. "Face Detection." In Computer Vision. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_798-1.

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Li, Stan Z., and Jianxin Wu. "Face Detection." In Handbook of Face Recognition. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-932-1_11.

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Gopalan, Raghuraman, William R. Schwartz, Rama Chellappa, and Ankur Srivastava. "Face Detection." In Visual Analysis of Humans. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-997-0_5.

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Yang, Ming-Hsuan. "Face Detection." In Encyclopedia of Biometrics. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-73003-5_87.

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Loy, Chen Change. "Face Detection." In Computer Vision. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63416-2_798.

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Yang, Ming-Hsuan. "Face Detection." In Encyclopedia of Biometrics. Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7488-4_87.

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Colmenarez, Antonio J., and Thomas S. Huang. "Face Detection and Recognition." In Face Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_9.

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Machado, Penousal, João Correia, and Juan Romero. "Improving Face Detection." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29139-5_7.

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Zorin, Arsenii, and Nikolay Abramov. "Disguised Face Detection." In Lecture Notes in Electrical Engineering. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1465-4_50.

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Conference papers on the topic "Face detection"

1

Bobulski, Janusz, and Mariusz Kubanek. "Fake Face Detection Using Deep Neural Network." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825810.

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Igene, Lambert, Afzal Hossain, Mohammad Zahir Uddin Chowdhury, et al. "Face Liveness Detection Competition (LivDet-Face) - 2024." In 2024 IEEE International Joint Conference on Biometrics (IJCB). IEEE, 2024. http://dx.doi.org/10.1109/ijcb62174.2024.10744462.

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Honarjoo, Narges, Fatemeh Taher, and Azadeh Mansouri. "Exploring Feature Map Correlations for Effective Fake Face Detection." In 2024 11th International Symposium on Telecommunications (IST). IEEE, 2024. https://doi.org/10.1109/ist64061.2024.10843660.

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Umadevi, M., S. Barghav Krishna, and N. Sai Kumar. "Deep Fake Face Detection using Efficient Convolutional Neural Networks." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00063.

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Madaan, Vijay, and Neha Sharma. "Detection of Real vs. Fake Face Enhanced by MobileNetV2." In 2025 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE). IEEE, 2025. https://doi.org/10.1109/iitcee64140.2025.10915444.

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Veneela, Thavisala, Sonti Vivek Sai, Yeturu Vamshidhar Reddy, and Vishnu Karthik Varma Kothapally. "Face Recognition from Partial Face Data and Fraud Detection." In 2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2024. https://doi.org/10.1109/cicn63059.2024.10847317.

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Wang, Yongwang, and Lian Pan. "YOLOV5s-Face face detection algorithm." In 2022 China Automation Congress (CAC). IEEE, 2022. http://dx.doi.org/10.1109/cac57257.2022.10054674.

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Alashbi, Abdulaziz Ali Saleh, Mohd Shahrizal Sunar, and Zieb Alqahtani. "Context-Aware Face Detection for Occluded Faces." In 2020 6th International Conference on Interactive Digital Media (ICIDM). IEEE, 2020. http://dx.doi.org/10.1109/icidm51048.2020.9339647.

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Edmunds, Taiamiti, and Alice Caplier. "Fake face detection based on radiometric distortions." In 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2016. http://dx.doi.org/10.1109/ipta.2016.7820995.

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Wang, Chengrui, and Weihong Deng. "Representative Forgery Mining for Fake Face Detection." In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2021. http://dx.doi.org/10.1109/cvpr46437.2021.01468.

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Reports on the topic "Face detection"

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Heisele, Bernd, Tomaso poggio, and Massimilinao Pontil. Face Detection in Still Gray Images. Defense Technical Information Center, 2000. http://dx.doi.org/10.21236/ada459705.

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Rowley, Henry A., Shumeet Baluja, and Takeo Kanade. Rotation Invariant Neural Network-Based Face Detection. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada341629.

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Sung, Kah K., and Tomaso Poggio. Example Based Learning for View-Based Human Face Detection. Defense Technical Information Center, 1994. http://dx.doi.org/10.21236/ada295738.

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Scassellati, Brian. Eye Finding via Face Detection for a Foveated, Active Vision System. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada455661.

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Тарасова, Олена Юріївна, and Ірина Сергіївна Мінтій. Web application for facial wrinkle recognition. Кривий Ріг, КДПУ, 2022. http://dx.doi.org/10.31812/123456789/7012.

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Facial recognition technology is named one of the main trends of recent years. It’s wide range of applications, such as access control, biometrics, video surveillance and many other interactive humanmachine systems. Facial landmarks can be described as key characteristics of the human face. Commonly found landmarks are, for example, eyes, nose or mouth corners. Analyzing these key points is useful for a variety of computer vision use cases, including biometrics, face tracking, or emotion detection. Different methods produce different facial landmarks. Some methods use only basic facial landmarks, while others bring out more detail. We use 68 facial markup, which is a common format for many datasets. Cloud computing creates all the necessary conditions for the successful implementation of even the most complex tasks. We created a web application using the Django framework, Python language, OpenCv and Dlib libraries to recognize faces in the image. The purpose of our work is to create a software system for face recognition in the photo and identify wrinkles on the face. The algorithm for determining the presence and location of various types of wrinkles and determining their geometric determination on the face is programmed.
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Polakowski, Michał, and Emma Quinn. Responses to irregularly staying migrants in Ireland. ESRI, 2022. http://dx.doi.org/10.26504/rs140.

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Irregularly staying migrants are more likely to face material deprivation, instability and are more vulnerable to exploitation and crime than legal residents (FRA, 2011). Ultimately, they may face deportation to their country of origin. The fear of detection and deportation can lead to underutilisation of public services (Vintila and Lafleur, 2020). The recent introduction of the Regularisation of Long-Term Undocumented Migrants Scheme (discussed below) is a major policy development that should improve the situation of many people living in Ireland. However, it is likely that irregular migration will persist, and related policy challenges will remain. This report aims to provide an overview of the situation of irregularly staying migrants in Ireland, including access to public services, and to outline major public debates and policy measures introduced to address related issues.
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Mishra, Shivangi, Steve Crookes, and Srijak Bhatnagar. Muskoxen and Genomics in the Community (MAGIC) Workshop: A Detailed Report. Arctic Institute of North America, 2025. https://doi.org/10.33174/aina2025tr04magicreport.

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This report provides an overview and summary of the Muskoxen and Genomics in the Community (MAGIC) Workshop held in Cambridge Bay, Nunavut in January 2024. The workshop brought together Inuit Knowledge Holders, hunters, and decision makers, scientists from a variety of disciplines and international participants. Together the group considered Inuit knowledge of and priorities for muskoxen, scientific knowledge and gaps, and the potential for genomic and DNA-based tools to help secure a viable future for muskoxen in the face of multiple climate-related stressors. Indigenous Knowledge combined with application of novel scientific advances, such as whole genome sequencing (WGS) of target organisms and use of DNA-based molecular ecological tools (such as environmental DNA markers for species detection from the soup of DNA molecules found within environmental samples), and consideration of the broader ecosystem offers the opportunity to change the way in which healthy wildlife populations are sustained.
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Wachs, Brandon. Satellite Image Deep Fake Creation and Detection. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1812627.

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Torralba, Antonio, and Pawan Sinha. Detecting Faces in Impoverished Images. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada636815.

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Zhou, Ziwei. Detecting Rater Effects Using Many-Facet Rasch Models and Bootstrap Techniques. Iowa State University, 2020. http://dx.doi.org/10.31274/cc-20240624-516.

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