Relevant bibliographies by topics / Face detection on thermal image

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Face detection on thermal image'

Author: Grafiati
Published: 28 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Face detection on thermal image"

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Seo, Jongwoo, and In-Jeong Chung. "Face Liveness Detection Using Thermal Face-CNN with External Knowledge." Symmetry 11, no. 3 (March 10, 2019): 360. http://dx.doi.org/10.3390/sym11030360.

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Face liveness detection is important for ensuring security. However, because faces are shown in photographs or on a display, it is difficult to detect the real face using the features of the face shape. In this paper, we propose a thermal face-convolutional neural network (Thermal Face-CNN) that knows the external knowledge regarding the fact that the real face temperature of the real person is 36~37 degrees on average. First, we compared the red, green, and blue (RGB) image with the thermal image to identify the data suitable for face liveness detection using a multi-layer neural network (MLP), convolutional neural network (CNN), and C-support vector machine (C-SVM). Next, we compared the performance of the algorithms and the newly proposed Thermal Face-CNN in a thermal image dataset. The experiment results show that the thermal image is more suitable than the RGB image for face liveness detection. Further, we also found that Thermal Face-CNN performs better than CNN, MLP, and C-SVM when the precision is slightly more crucial than recall through F-measure.
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Albar, Albar, Hendrick Hendrick, and Rahmad Hidayat. "Segmentation Method for Face Modelling in Thermal Images." Knowledge Engineering and Data Science 3, no. 2 (December 31, 2020): 99. http://dx.doi.org/10.17977/um018v3i22020p99-105.

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Face detection is mostly applied in RGB images. The object detection usually applied the Deep Learning method for model creation. One method face spoofing is by using a thermal camera. The famous object detection methods are Yolo, Fast RCNN, Faster RCNN, SSD, and Mask RCNN. We proposed a segmentation Mask RCNN method to create a face model from thermal images. This model was able to locate the face area in images. The dataset was established using 1600 images. The images were created from direct capturing and collecting from the online dataset. The Mask RCNN was configured to train with 5 epochs and 131 iterations. The final model predicted and located the face correctly using the test image.
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Ma, Chao, Ngo Trung, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, and Rin-ichiro Taniguchi. "Adapting Local Features for Face Detection in Thermal Image." Sensors 17, no. 12 (November 27, 2017): 2741. http://dx.doi.org/10.3390/s17122741.

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Kowalski, Marcin, and Krzysztof Mierzejewski. "Detection of 3D face masks with thermal infrared imaging and deep learning techniques." Photonics Letters of Poland 13, no. 2 (June 30, 2021): 22. http://dx.doi.org/10.4302/plp.v13i2.1091.

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Biometric systems are becoming more and more efficient due to increasing performance of algorithms. These systems are also vulnerable to various attacks. Presentation of falsified identity to a biometric sensor is one the most urgent challenges for the recent biometric recognition systems. Exploration of specific properties of thermal infrared seems to be a comprehensive solution for detecting face presentation attacks. This letter presents outcome of our study on detecting 3D face masks using thermal infrared imaging and deep learning techniques. We demonstrate results of a two-step neural network-featured method for detecting presentation attacks. Full Text: PDF ReferencesS.R. Arashloo, J. Kittler, W. Christmas, "Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features", IEEE Trans. Inf. Forensics Secur. 10, 11 (2015). CrossRef A. Anjos, M.M. Chakka, S. Marcel, "Motion-based counter-measures to photo attacks inface recognition", IET Biometrics 3, 3 (2014). CrossRef M. Killioǧlu, M. Taşkiran, N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking", Proc. SAMI IEEE, (2017). CrossRef A. Asaduzzaman, A. Mummidi, M.F. Mridha, F.N. Sibai, "Improving facial recognition accuracy by applying liveness monitoring technique", Proc. ICAEE IEEE, (2015). CrossRef M. Kowalski, "A Study on Presentation Attack Detection in Thermal Infrared", Sensors 20, 14 (2020). CrossRef C. Galdi, et al, "PROTECT: Pervasive and useR fOcused biomeTrics bordEr projeCT - a case study", IET Biometrics 9, 6 (2020). CrossRef D.A. Socolinsky, A. Selinger, J. Neuheisel, "Face recognition with visible and thermal infrared imagery", Comput. Vis Image Underst. 91, 1-2 (2003) CrossRef L. Sun, W. Huang, M. Wu, "TIR/VIS Correlation for Liveness Detection in Face Recognition", Proc. CAIP, (2011). CrossRef J. Seo, I. Chung, "Face Liveness Detection Using Thermal Face-CNN with External Knowledge", Symmetry 2019, 11, 3 (2019). CrossRef A. George, Z. Mostaani, D Geissenbuhler, et al., "Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network", IEEE Trans. Inf. Forensics Secur. 15, (2020). CrossRef S. Ren, K. He, R. Girshick, J. Sun, "Proceedings of IEEE Conference on Computer Vision and Pattern Recognition", Proc. CVPR IEEE 39, (2016). CrossRef K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition", Proc. CVPR, (2016). CrossRef K. Mierzejewski, M. Mazurek, "A New Framework for Assessing Similarity Measure Impact on Classification Confidence Based on Probabilistic Record Linkage Model", Procedia Manufacturing 44, 245-252 (2020). CrossRef
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Cho, Se, Na Baek, Min Kim, Ja Koo, Jong Kim, and Kang Park. "Face Detection in Nighttime Images Using Visible-Light Camera Sensors with Two-Step Faster Region-Based Convolutional Neural Network." Sensors 18, no. 9 (September 7, 2018): 2995. http://dx.doi.org/10.3390/s18092995.

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Conventional nighttime face detection studies mostly use near-infrared (NIR) light cameras or thermal cameras, which are robust to environmental illumination variation and low illumination. However, for the NIR camera, it is difficult to adjust the intensity and angle of the additional NIR illuminator according to its distance from an object. As for the thermal camera, it is expensive to use as a surveillance camera. For these reasons, we propose a nighttime face detection method based on deep learning using a single visible-light camera. In a long-distance night image, it is difficult to detect faces directly from the entire image due to noise and image blur. Therefore, we propose Two-Step Faster region-based convolutional neural network (R-CNN) based on the image preprocessed by histogram equalization (HE). As a two-step scheme, our method sequentially performs the detectors of body and face areas, and locates the face inside a limited body area. By using our two-step method, the processing time by Faster R-CNN can be reduced while maintaining the accuracy of face detection by Faster R-CNN. Using a self-constructed database called Dongguk Nighttime Face Detection database (DNFD-DB1) and an open database of Fudan University, we proved that the proposed method performs better compared to other existing face detectors. In addition, the proposed Two-Step Faster R-CNN outperformed single Faster R-CNN and our method with HE showed higher accuracies than those without our preprocessing in nighttime face detection.
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Latinović, Nikola, Tijana Vuković, Ranko Petrović, Miloš Pavlović, Marko Kadijević, Ilija Popadić, and Mladen Veinović. "Implementation challenge and analysis of thermal image degradation on R-CNN face detection." Telfor Journal 12, no. 2 (2020): 98–103. http://dx.doi.org/10.5937/telfor2002098l.

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Face detection systems with color cameras were rapidly evolving and have been well researched. In environments with good visibility they can reach excellent accuracy. But changes in illumination conditions can result in performance degradation, which is the one of the major limitations in visible light face detection systems. The solution to this problem could be in using thermal infrared cameras, since their operation doesn't depend on illumination. Recent studies have shown that deep learning methods can achieve an impressive performance on object detection tasks, and face detection in particular. The goal of this paper is to find an effective way to take advantages from thermal infrared spectra and provide an analysis of various image degradation influence on thermal face detection performance in a system based on R-CNN with special accent on implementation on a hardware platform for video signal processing that institute Vlatacom has developed, called vVSP.
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Fitriyah, Hurriyatul, and Edita Rosana Widasari. "Face Detection of Thermal Images in Various Standing Body-Pose using Facial Geometry." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 14, no. 4 (October 31, 2020): 407. http://dx.doi.org/10.22146/ijccs.59672.

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Automatic face detection in frontal view for thermal images is a primary task in a health system e.g. febrile identification or security system e.g. intruder recognition. In a daily state, the scanned person does not always stay in frontal face view. This paper develops an algorithm to identify a frontal face in various standing body-pose. The algorithm used an image processing method where first it segmented face based on human skin’s temperature. Some exposed non-face body parts could also get included in the segmentation result, hence discriminant features of a face were applied. The shape features were based on the characteristic of a frontal face, which are: (1) Size of a face, (2) facial Golden Ratio, and (3) Shape of a face is oval. The algorithm was tested on various standing body-pose that rotate 360° towards 2 meters and 4 meters camera-to-object distance. The accuracy of the algorithm on face detection in a manageable environment is 95.8%. It detected face whether the person was wearing glasses or not.
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van Doremalen, Rob F. M., Jaap J. van Netten, Jeff G. van Baal, Miriam M. R. Vollenbroek-Hutten, and Ferdinand van der Heijden. "Infrared 3D Thermography for Inflammation Detection in Diabetic Foot Disease: A Proof of Concept." Journal of Diabetes Science and Technology 14, no. 1 (June 14, 2019): 46–54. http://dx.doi.org/10.1177/1932296819854062.

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Background: Thermal assessment of the plantar surface of the foot using spot thermometers and thermal imaging has been proven effective in diabetic foot ulcer prevention. However, with traditional cameras this is limited to single spots or a two-dimensional (2D) view of the plantar side of foot, where only 50% of the ulcers occur. To improve ulcer detection, the view has to be extended beyond 2D. Our aim is to explore for proof of concept the combination of three-dimensional (3D) models with thermal imaging for inflammation detection in diabetic foot disease. Method: From eight participants with a current diabetic foot ulcer we simultaneously acquired a 3D foot model and three thermal infrared images using a high-resolution medical 3D imaging system aligned with three smartphone-based thermal infrared cameras. Using spatial transformations, we aimed to map thermal images onto the 3D model, to create the 3D visualizations. Expert clinicians assessed these for quality and face validity as +, +/-, -. Results: We could replace the texture maps (color definitions) of the 3D model with the thermal infrared images and created the first-ever 3D thermographs of the diabetic foot. We then converted these models to 3D PDF-files compatible with the hospital IT environment. Face validity was assessed as + in six and +/- in two cases. Conclusions: We have provided a proof of concept for the creation of clinically useful 3D thermal foot images to assess the diabetic foot skin temperature in 3D in a hospital IT environment. Future developments are expected to improve the image-processing techniques to result in easier, handheld applications and driving further research.
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Bedoya-Echeverry, Sebastián, Hernán Belalcázar-Ramírez, Humberto Loaiza-Correa, Sandra Esperanza Nope-Rodríguez, Carlos Rafael Pinedo-Jaramillo, and Andrés David Restrepo-Girón. "Detection of lies by facial thermal imagery analysis." Revista Facultad de Ingeniería 26, no. 44 (January 25, 2017): 45. http://dx.doi.org/10.19053/01211129.v26.n44.2017.5771.

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An artificial vision system is presented for lie detection by analyzing face thermal image sequences. This system represents an alternative technique to the polygraph. Some of its features are: 1) it has no physical contact with the examinee, 2) it is non-intrusive, 3) it has a potential for private use, and 4) it can simultaneously analyze several persons. The proposed system is based on the detection of physiological changes in temperature in the lacrimal puncta area caused by the subtle increase in blood flow through the nearby vascular network. These changes take place when anxiety appears as a consequence of deception. Thus, the system segments the periorbital area, and tracks consecutive frames using the Kanade-Lucas-Tomasi algorithm. The results show a success rate of 79.2 % in detecting lies using a simple classification based on the comparison between the estimated temperatures in control questions, and the rest of the interrogation procedure. The performance of this system is comparable with previous works, where cameras with better specifications were used.
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Kopaczka, Marcin, Lukas Breuer, Justus Schock, and Dorit Merhof. "A Modular System for Detection, Tracking and Analysis of Human Faces in Thermal Infrared Recordings." Sensors 19, no. 19 (September 24, 2019): 4135. http://dx.doi.org/10.3390/s19194135.

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We present a system that utilizes a range of image processing algorithms to allow fully automated thermal face analysis under both laboratory and real-world conditions. We implement methods for face detection, facial landmark detection, face frontalization and analysis, combining all of these into a fully automated workflow. The system is fully modular and allows implementing own additional algorithms for improved performance or specialized tasks. Our suggested pipeline contains a histogtam of oriented gradients support vector machine (HOG-SVM) based face detector and different landmark detecion methods implemented using feature-based active appearance models, deep alignment networks and a deep shape regression network. Face frontalization is achieved by utilizing piecewise affine transformations. For the final analysis, we present an emotion recognition system that utilizes HOG features and a random forest classifier and a respiratory rate analysis module that computes average temperatures from an automatically detected region of interest. Results show that our combined system achieves a performance which is comparable to current stand-alone state-of-the-art methods for thermal face and landmark datection and a classification accuracy of 65.75% for four basic emotions.
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Dissertations / Theses on the topic "Face detection on thermal image"

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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.

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Mestrado em Engenharia Eletrónica e Telecomunicações
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.
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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Roman, Matej. "Automatizované měření teploty v boji proti COVID." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2021. http://www.nusl.cz/ntk/nusl-442439.

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This thesis focuses on the development of an open source software capable of automatic face detection in an image captured by a thermal camera, followed by a temperature measuring. This software is supposed to aid in the COVID-19 pandemics. The developed software is independent of used thermal camera. In this thesis, I am using TIM400 thermal camera. The implementation of the face detection was achieved by an OpenCV module. The methods tested were Template Matching, Eigen Faces, and Cascade Classifier. The last-mentioned had the best results, hence was used in the final version of the software. Cascade Classifier is looking for the eyes and their surrounding area in the image, allowing the software to subsequently measure the temperature on the surface of one's forehead. One can therefore be wearing a face mask or a respirator safely. The temperature measuring works in real time and the software is able to capture several people at once. It then keeps a record of the temperature of each measured individual as well as the time of the measurement. The software as a whole is a part of an installation file compatible with the Windows operating system. The functionality of this software was tested – the video recordings are included in this thesis.
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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.
SIPL, Mechatronics, GIST 1 Oryong-Dong, Buk-Gu, Gwangju, 500-712 South Korea tel. 0082-62-970-2997
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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/.

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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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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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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.

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.

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.

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.

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Yigit, Ahmet. "Thermal And Visible Band Image Fusion For Abandoned Object Detection." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611720/index.pdf.

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Packages that are left unattended in public spaces are a security concern and timely detection of these packages is important for prevention of potential threats. Operators should be always alert to detect abandoned items in crowded environments. However, it is very difficult for operators to stay concentrated for extended periods. Therefore, it is important to aid operators with automatic detection of abandoned items. Most of the methods in the literature define abandoned items as items newly added to the scene and stayed stationary for a predefined time. Hence other stationary objects, such as people sitting on a bench are also detected as suspicious objects resulting in a high number of false alarms. These false alarms could be prevented by discriminating suspicious items as living/nonliving objects. In this thesis, visible band and thermal band cameras are used together to analyze the interactions between humans and other objects. Thermal images help classification of objects using their heat signatures. This way, people and the objects they carry or left behind can be detected separately. Especially, it is aimed to detect abandoned items and discriminate living or nonliving objects
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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." University of Sydney. Electrical and Information Engineering, 2004. http://hdl.handle.net/2123/586.

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Human face recognition is an important area in the field of biometrics. It has been an active area of research for several decades, but still remains a challenging problem because of the complexity of the human face. In this thesis we describe fully automatic solutions that can locate faces and then perform identification and verification. We present a solution for face localisation using eye locations. We derive an efficient representation for the decision hyperplane of linear and nonlinear Support Vector Machines (SVMs). For this we introduce the novel concept of $\rho$ and $\eta$ prototypes. The standard formulation for the decision hyperplane is reformulated and expressed in terms of the two prototypes. Different kernels are treated separately to achieve further classification efficiency and to facilitate its adaptation to operate with the fast Fourier transform to achieve fast eye detection. Using the eye locations, we extract and normalise the face for size and in-plane rotations. Our method produces a more efficient representation of the SVM decision hyperplane than the well-known reduced set methods. As a result, our eye detection subsystem is faster and more accurate. The use of fractals and fractal image coding for object recognition has been proposed and used by others. Fractal codes have been used as features for recognition, but we need to take into account the distance between codes, and to ensure the continuity of the parameters of the code. We use a method based on fractal image coding for recognition, which we call the Fractal Neighbour Distance (FND). The FND relies on the Euclidean metric and the uniqueness of the attractor of a fractal code. An advantage of using the FND over fractal codes as features is that we do not have to worry about the uniqueness of, and distance between, codes. We only require the uniqueness of the attractor, which is already an implied property of a properly generated fractal code. Similar methods to the FND have been proposed by others, but what distinguishes our work from the rest is that we investigate the FND in greater detail and use our findings to improve the recognition rate. Our investigations reveal that the FND has some inherent invariance to translation, scale, rotation and changes to illumination. These invariances are image dependent and are affected by fractal encoding parameters. The parameters that have the greatest effect on recognition accuracy are the contrast scaling factor, luminance shift factor and the type of range block partitioning. The contrast scaling factor affect the convergence and eventual convergence rate of a fractal decoding process. We propose a novel method of controlling the convergence rate by altering the contrast scaling factor in a controlled manner, which has not been possible before. This helped us improve the recognition rate because under certain conditions better results are achievable from using a slower rate of convergence. We also investigate the effects of varying the luminance shift factor, and examine three different types of range block partitioning schemes. They are Quad-tree, HV and uniform partitioning. We performed experiments using various face datasets, and the results show that our method indeed performs better than many accepted methods such as eigenfaces. The experiments also show that the FND based classifier increases the separation between classes. The standard FND is further improved by incorporating the use of localised weights. A local search algorithm is introduced to find a best matching local feature using this locally weighted FND. The scores from a set of these locally weighted FND operations are then combined to obtain a global score, which is used as a measure of the similarity between two face images. Each local FND operation possesses the distortion invariant properties described above. Combined with the search procedure, the method has the potential to be invariant to a larger class of non-linear distortions. We also present a set of locally weighted FNDs that concentrate around the upper part of the face encompassing the eyes and nose. This design was motivated by the fact that the region around the eyes has more information for discrimination. Better performance is achieved by using different sets of weights for identification and verification. For facial verification, performance is further improved by using normalised scores and client specific thresholding. In this case, our results are competitive with current state-of-the-art methods, and in some cases outperform all those to which they were compared. For facial identification, under some conditions the weighted FND performs better than the standard FND. However, the weighted FND still has its short comings when some datasets are used, where its performance is not much better than the standard FND. To alleviate this problem we introduce a voting scheme that operates with normalised versions of the weighted FND. Although there are no improvements at lower matching ranks using this method, there are significant improvements for larger matching ranks. Our methods offer advantages over some well-accepted approaches such as eigenfaces, neural networks and those that use statistical learning theory. Some of the advantages are: new faces can be enrolled without re-training involving the whole database; faces can be removed from the database without the need for re-training; there are inherent invariances to face distortions; it is relatively simple to implement; and it is not model-based so there are no model parameters that need to be tweaked.
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Rondahl, Thomas. "Face Detection in Digital Imagery Using Computer Vision and Image Processing." Thesis, Umeå universitet, Institutionen för datavetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-51406.

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By adding a failure fault limit to an existing implementation of a face detection system application and a tolerance limit for detection time, a desired throughput for detected objects could be established. The aim of this thesis was to add an increased detection rate for pro le/partial faces while increasing the stability and run-time of the system. The results were obtained through an empirical analysis of test data which was compared between the implementation done for this thesis and the older implementation. The results showed an increase in detected faces (in low sized images) by 10% while also increasing the number of false-positives by 0.725 detections per average image. In large size image cases, an automatic scaling functionality was added, to decrease detection time and decrease false-negatives. The results indicated a decrease in average detection time from (old implementation) 15 seconds to 2 seconds, while still increasing positive detection with 23%, from an average of 42% to 65%. False-positives were also decreased from 5.8 to 0.2 detections per average image used in test.
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Patel, Brindal A. "R-Eye| An image processing-based embedded system for face detection and tracking." Thesis, California State University, Long Beach, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10141532.

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The current project presents the development of R-Eye, a face detection and tracking system implemented as an embedded device based on the Arduino microcontroller. The system is programmed in Python using the Viola-Jones algorithm for image processing. Several experiments designed to measure and compare the performance of the system under various conditions show that the system performs well when used with an integrated camera, reaching a 93% face recognition accuracy for a clear face. The accuracy is lower when detecting a face with accessories, such as a pair of eyeglasses (80%), or when a low-resolution low-quality camera is used. Experimental results also show that the system is capable of detecting and tracking a face within a frame containing multiple faces.

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Schwambach, Costa Vítor. "Optimization of a face detection algorithm for real-time mobile phone applications." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/2335.

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Made available in DSpace on 2014-06-12T15:56:57Z (GMT). No. of bitstreams: 2 arquivo3096_1.pdf: 4031500 bytes, checksum: 3cfbafa985058f2171a93b3e230c2c35 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2009
Desde equipamentos de vigillância por vídeo a câmeras digitais e telefones celulares, a detecção de rostos e uma funcionalidade que esta rapidamente ganhando peso no projeto de interfaces de usuario mais inteligentes e tornando a interação homem-maquina cada vez mais natural e intuitiva. Com isto em mente, fabricantes de chips estão embarcando esta tecnologia na sua nova geração de processadores de sinal de imagem (ISP) desenvolvidos especificamente para uso em aparelhos celulares. O foco deste trabalho foi analisar um algoritmo para detecção de rostos para suportar a definição da arquitetura mais adequada a ser usada na solução final. Um algoritmo inicial baseado na tecnica de Cascata de Caracteristicas Simples foi usado como base para este trabalho. O algoritmo inicial, como especificado, leva quase quarenta segundos para processar um unico quadro de imagem no processador alvo, tempo este que inviabilizaria o uso desta solução. Focando na implementação de um novo ISP, o algoritmo foi completamente reescrito, otimizado e propriamente mapeado na plataforma alvo, ao ponto onde um fator de aceleração de 167x foi atingido e uma imagem de pior caso agora leva menos de 250 milissegundos para ser processada. Este numero e ainda mais baixo se for considerada a media em um conjunto maior de imagens ou um vídeo, caindo para cerca de 100 milissegundos por quadro de imagem processado. Não obstante, performance não foi o unico alvo, tambem a quantidade de memoria necessaria foi dramaticamente reduzida. Isto tem um impacto direto na area de silicio requerida pelo circuito e conseq uentemente menores custos de producao e consumo de potência, fatores criticos em um sistema para aplicações moveis. E importante ressaltar que a qualidade não foi deixada de lado e em todas as otimizações realizadas, tomou-se o cuidado de verificar que a qualidade de detecção não tinha sido impactada. Este documento apresenta a pesquisa feita e os resultados obtidos. Começa por uma breve introdução ao assunto de Visão Computacional e aos desafios de projetar uma solução de detecção de rostos. Apos esta introdução, o algoritmo que serviu como base para este trabalho e apresentado juntamente com as otimizações mais relevantes ao nivel algoritmico para melhorar a performance. Na sequência, instruções customizadas desenvolvidas para acelerar a execução do algoritmo na solução final são apresentadas e discutidas
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Books on the topic "Face detection on thermal image"

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

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

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Smolka, Bogdan, Emre Celebi, and Michal Kawulok. Advances in Face Detection and Facial Image Analysis. Springer, 2018.

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Ahuja, Narendra, and Ming-Hsuan Yang. Face Detection and Gesture Recognition for Human-Computer Interaction (The International Series in Video Computing). Springer, 2001.

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Book chapters on the topic "Face detection on thermal image"

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Silva, Gustavo, Rui Monteiro, André Ferreira, Pedro Carvalho, and Luís Corte-Real. "Face Detection in Thermal Images with YOLOv3." In Advances in Visual Computing, 89–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33723-0_8.

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Forczmański, Paweł. "Human Face Detection in Thermal Images Using an Ensemble of Cascading Classifiers." In Hard and Soft Computing for Artificial Intelligence, Multimedia and Security, 205–15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48429-7_19.

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Kopaczka, Marcin, Özcan Özkan, and Dorit Merhof. "Face Tracking and Respiratory Signal Analysis for the Detection of Sleep Apnea in Thermal Infrared Videos with Head Movement." In New Trends in Image Analysis and Processing – ICIAP 2017, 163–70. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70742-6_15.

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Kopaczka, Marcin, Jan Nestler, and Dorit Merhof. "Face Detection in Thermal Infrared Images: A Comparison of Algorithm- and Machine-Learning-Based Approaches." In Advanced Concepts for Intelligent Vision Systems, 518–29. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70353-4_44.

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Choraś, Ryszard S. "Thermal Face Recognition." In Image Processing and Communications Challenges 7, 37–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23814-2_5.

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Rihan, Jonathan, Pushmeet Kohli, and Philip H. S. Torr. "OBJCUT for Face Detection." In Computer Vision, Graphics and Image Processing, 576–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11949619_51.

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Forczmański, Paweł, and Anton Smoliński. "Eyes State Detection in Thermal Imaging." In Image Processing and Communications, 22–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31254-1_4.

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Kirdak, Varsha, and Sudhir Vegad. "Face Image Detection Methods: A Survey." In Advances in Intelligent Systems and Computing, 209–16. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5272-9_20.

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Anisetti, Marco, Valerio Bellandi, Ernesto Damiani, Luigi Arnone, and Benoit Rat. "A3FD: Accurate 3D Face Detection." In Signal Processing for Image Enhancement and Multimedia Processing, 155–65. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-72500-0_14.

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Wang, Chu, Xiaoqiang Li, and Wenfeng Wang. "Image Fusion for Improving Thermal Human Face Image Recognition." In Communications in Computer and Information Science, 417–27. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2336-3_39.

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Conference papers on the topic "Face detection on thermal image"

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Ma, Chao, Ngo Thanh Trung, Hideaki Uchiyama, Hajime Nagahara, Atsushi Shimada, and Rin-ichiro Taniguchi. "Mixed features for face detection in thermal image." In The International Conference on Quality Control by Artificial Vision 2017, edited by Hajime Nagahara, Kazunori Umeda, and Atsushi Yamashita. SPIE, 2017. http://dx.doi.org/10.1117/12.2266836.

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Wong, Wai Kit, Joe How Hui, Jalil Bin Md Desa, Nur Izzati Nadiah Binti Ishak, Azlan Bin Sulaiman, and Yante Binti Mohd Nor. "Face detection in thermal imaging using head curve geometry." In 2012 5th International Congress on Image and Signal Processing (CISP). IEEE, 2012. http://dx.doi.org/10.1109/cisp.2012.6469684.

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Hussien, M. Naeem, Mohd-Haris Lye, Mohammad Faizal Ahmad Fauzi, Tan Ching Seong, and Sarina Mansor. "Comparative analysis of eyes detection on face thermal images." In 2017 IEEE International Conference on Signal and Image Processing Applications (ICSIPA). IEEE, 2017. http://dx.doi.org/10.1109/icsipa.2017.8120641.

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Kwaśniewska, A., and J. Rumiński. "Face detection in image sequences using a portable thermal camera." In 2016 Quantitative InfraRed Thermography. QIRT Council, 2016. http://dx.doi.org/10.21611/qirt.2016.071.

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Vukovic, Tijana, Ranko Petrovic, Milos Pavlovic, and Srdan Stankovic. "Thermal Image Degradation Influence on R-CNN Face Detection Performance." In 2019 27th Telecommunications Forum (TELFOR). IEEE, 2019. http://dx.doi.org/10.1109/telfor48224.2019.8971128.

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Kopaczka, Marcin, Justus Schock, Jan Nestler, Kevin Kielholz, and Dorit Merhof. "A combined modular system for face detection, head pose estimation, face tracking and emotion recognition in thermal infrared images." In 2018 IEEE International Conference on Imaging Systems and Techniques (IST). IEEE, 2018. http://dx.doi.org/10.1109/ist.2018.8577124.

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Takahashi, Tsuyoshi, Bo Wu, Yoichi Kageyama, Makoto Nishida, and Masaki Ishii. "A study of learning data size for automatic face area detection in sequential thermal images." In 2015 IEEE 4th Global Conference on Consumer Electronics (GCCE). IEEE, 2015. http://dx.doi.org/10.1109/gcce.2015.7398530.

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Kopaczka, Marcin, Kemal Acar, and Dorit Merhof. "Robust Facial Landmark Detection and Face Tracking in Thermal Infrared Images using Active Appearance Models." In International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0005716801500158.

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Zheng, Yufeng. "Face detection and eyeglasses detection for thermal face recognition." In IS&T/SPIE Electronic Imaging, edited by Philip R. Bingham and Edmund Y. Lam. SPIE, 2012. http://dx.doi.org/10.1117/12.907123.

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Colmenarez, Antonio J., and Thomas S. Huang. "Frontal-view face detection." In Visual Communications and Image Processing '95, edited by Lance T. Wu. SPIE, 1995. http://dx.doi.org/10.1117/12.206630.

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Reports on the topic "Face detection on thermal image"

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Duberstein, Corey A., Shari Matzner, Valerie I. Cullinan, Daniel J. Virden, Joshua R. Myers, and Adam R. Maxwell. Automated Thermal Image Processing for Detection and Classification of Birds and Bats - FY2012 Annual Report. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1076723.

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Clausen, Jay, Susan Frankenstein, Jason Dorvee, Austin Workman, Blaine Morriss, Keran Claffey, Terrance Sobecki, et al. Spatial and temporal variance of soil and meteorological properties affecting sensor performance—Phase 2. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/41780.

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An approach to increasing sensor performance and detection reliability for buried objects is to better understand which physical processes are dominant under certain environmental conditions. The present effort (Phase 2) builds on our previously published prior effort (Phase 1), which examined methods of determining the probability of detection and false alarm rates using thermal infrared for buried-object detection. The study utilized a 3.05 × 3.05 m test plot in Hanover, New Hampshire. Unlike Phase 1, the current effort involved removing the soil from the test plot area, homogenizing the material, then reapplying it into eight discrete layers along with buried sensors and objects representing targets of inter-est. Each layer was compacted to a uniform density consistent with the background undisturbed density. Homogenization greatly reduced the microscale soil temperature variability, simplifying data analysis. The Phase 2 study spanned May–November 2018. Simultaneous measurements of soil temperature and moisture (as well as air temperature and humidity, cloud cover, and incoming solar radiation) were obtained daily and recorded at 15-minute intervals and coupled with thermal infrared and electro-optical image collection at 5-minute intervals.
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