Relevant bibliographies by topics / Face detection

Contents

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

Academic literature on the topic 'Face detection'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 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 h
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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 perfo
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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
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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 reve
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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 ro
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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 ro
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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
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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
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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 classifica
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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 vers
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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 d
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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 detec
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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 des
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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
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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
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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 des
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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 firs
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Books on the topic "Face detection"

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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"

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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"

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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 landmar
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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 migrati
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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 wi
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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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