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Relevant bibliographies by topics / Human face recognition

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Academic literature on the topic 'Human face recognition'

Author: Grafiati

Published: 4 June 2021

Last updated: 20 February 2023

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Journal articles on the topic "Human face recognition"

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Sabharwal, Himani, and Akash Tayal. "Human Face Recognition." International Journal of Computer Applications 104, no. 11 (2014): 1–3. http://dx.doi.org/10.5120/18243-9173.

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S.G, Rajeshwari. "Human Face Recognition." International Journal for Research in Applied Science and Engineering Technology 8, no. 6 (2020): 638–43. http://dx.doi.org/10.22214/ijraset.2020.6104.

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Choudhury, Debesh. "Three-dimensional human face recognition." Journal of Optics 38, no. 1 (2009): 16–21. http://dx.doi.org/10.1007/s12596-009-0002-0.

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Rossion, B. "Neurophysiology of human face recognition." Neurophysiologie Clinique 49, no. 4 (2019): 345. http://dx.doi.org/10.1016/j.neucli.2019.07.010.

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Shyam, Radhey, and Yogendra Narain Singh. "Multialgorithmic Frameworks for Human Face Recognition." Journal of Electrical and Computer Engineering 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/4645971.

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This paper presents a critical evaluation of multialgorithmic face recognition systems for human authentication in unconstrained environment. We propose different frameworks of multialgorithmic face recognition system combining holistic and texture methods. Our aim is to combine the uncorrelated methods of the face recognition that supplement each other and to produce a comprehensive representation of the biometric cue to achieve optimum recognition performance. The multialgorithmic frameworks are designed to combine different face recognition methods such as (i) Eigenfaces and local binary pa

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Allison, T., H. Ginter, G. McCarthy, et al. "Face recognition in human extrastriate cortex." Journal of Neurophysiology 71, no. 2 (1994): 821–25. http://dx.doi.org/10.1152/jn.1994.71.2.821.

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1. Twenty-four patients with electrodes chronically implanted on the surface of extrastriate visual cortex viewed faces, equiluminant scrambled faces, cars, scrambled cars, and butterflies. 2. A surface-negative potential, N200, was evoked by faces but not by the other categories of stimuli. N200 was recorded only from small regions of the left and right fusiform and inferior temporal gyri. Electrical stimulation of the same region frequently produced a temporary inability to name familiar faces. 3. The results suggest that discrete regions of inferior extrastriate visual cortex, varying in lo

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Singh, Pankaj, Sanjay Kumar Singh, and Nidhi Gaba. "YCbCr Technique based Human Face Recognition." International Journal of Advance Research and Innovation 3, no. 1 (2015): 79–83. http://dx.doi.org/10.51976/ijari.311514.

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Face detection is a necessary first-step in face recognition systems, with the reason of localize and extract the face region from the background. It also has a number of applications in areas such as content-based image recovery, video coding, video conferencing, crowd observation, and intelligent human–computer interfaces. We have taken skin color as a tool for detection. This technique works well for all types’ faces which have a specific complexion varying under definite range. We have taken real life examples and simulated the algorithms in MATLAB successfully. This paper concentrates on

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Bhange, Prof Anup. "Face Detection System with Face Recognition." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 1095–100. http://dx.doi.org/10.22214/ijraset.2022.39976.

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Abstract: The face is one of the easiest way to distinguish the individual identity of each other. Face recognition is a personal identification system that uses personal characteristics of a person to identify the person's identity. Now a days Human Face Detection and Recognition become a major field of interest in current research because there is no deterministic algorithm to find faces in a given image. 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

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Abbas, Hawraa H., Bilal Z. Ahmed, and Ahmed Kamil Abbas. "3D Face Factorisation for Face Recognition Using Pattern Recognition Algorithms." Cybernetics and Information Technologies 19, no. 2 (2019): 28–37. http://dx.doi.org/10.2478/cait-2019-0013.

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Abstract The face is the preferable biometrics for person recognition or identification applications because person identifying by face is a human connate habit. In contrast to 2D face recognition, 3D face recognition is practically robust to illumination variance, facial cosmetics, and face pose changes. Traditional 3D face recognition methods describe shape variation across the whole face using holistic features. In spite of that, taking into account facial regions, which are unchanged within expressions, can acquire high performance 3D face recognition system. In this research, the recognit

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Liu, Haisong, Minxian Wu, Guofan Jin, Gang Cheng, and Qingsheng He. "An automatic human face recognition system." Optics and Lasers in Engineering 30, no. 3-4 (1998): 305–14. http://dx.doi.org/10.1016/s0143-8166(98)00022-0.

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Dissertations / Theses on the topic "Human face recognition"

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Wong, Vincent. "Human face recognition /." Online version of thesis, 1994. http://hdl.handle.net/1850/11882.

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Ener, Emrah. "Recognition Of Human Face Expressions." Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/3/12607521/index.pdf.

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In this study a fully automatic and scale invariant feature extractor which does not require manual initialization or special equipment is proposed. Face location and size is extracted using skin segmentation and ellipse fitting. Extracted face region is scaled to a predefined size, later upper and lower facial templates are used for feature extraction. Template localization and template parameter calculations are carried out using Principal Component Analysis. Changes in facial feature coordinates between analyzed image and neutral expression image are used for expression classification. Perf

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Batur, Aziz Umit. "Illumination-robust face recognition." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/15440.

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Zou, Weiwen. "Face recognition from video." HKBU Institutional Repository, 2012. https://repository.hkbu.edu.hk/etd_ra/1431.

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Lee, Colin K. "Infrared face recognition." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Jun%5FLee%5FColin.pdf.

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Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2004.<br>Thesis advisor(s): Monique P. Fargues, Gamani Karunasiri. Includes bibliographical references (p. 135-136). Also available online.

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Gangam, Priyanka Reddy. "Recognizing Face Sketches by Human Volunteers." Youngstown State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1297198615.

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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

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Tan, Teewoon. "HUMAN FACE RECOGNITION BASED ON FRACTAL IMAGE CODING." Thesis, The University of Sydney, 2003. 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

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Tibbalds, Adam Dominic. "Three dimensional human face acquisition for recognition." Thesis, University of Cambridge, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.624854.

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Low, Boon Kee. "Computer extraction of human faces." Thesis, De Montfort University, 1999. http://hdl.handle.net/2086/10668.

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Due to the recent advances in visual communication and face recognition technologies, automatic face detection has attracted a great deal of research interest. Being a diverse problem, the development of face detection research has comprised contributions from researchers in various fields of sciences. This thesis examines the fundamentals of various face detection techniques implemented since the early 70's. Two groups of techniques are identified based on their approach in applying face knowledge as a priori: feature-based and image-based. One of the problems faced by the current feature-bas

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Books on the topic "Human face recognition"

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Dutta, Paramartha, and Asit Barman. Human Emotion Recognition from Face Images. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3883-4.

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Handbook of face recognition. 2nd ed. Springer, 2011.

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Quaglia, Adamo, and Calogera M. Epifano. Face recognition: Methods, applications, and technology. Nova Science, 2012.

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Grother, Patrick J. Face recognition vendor test 2002 performance metrics. U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2003.

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1948-, Wechsler Harry, North Atlantic Treaty Organization. Scientific Affairs Division., and NATO Advanced Study Institute on Face Recognition: From Theory to Applications (1997 : Stirling, Stirling, Scotland), eds. Face recognition: From theory to applications. Springer, 1998.

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Daijin, Kim. Automated face analysis: Emerging technologies and research. Information Science Reference, 2009.

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Uwechue, Okechukwu A. Human face recognition using third-order synthetic neural networks. Kluwer Academic Publishers, 1997.

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Uwechue, Okechukwu A. Human Face Recognition Using Third-Order Synthetic Neural Networks. Springer US, 1997.

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Uwechue, Okechukwu A., and Abhijit S. Pandya. Human Face Recognition Using Third-Order Synthetic Neural Networks. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-4092-2.

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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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Book chapters on the topic "Human face recognition"

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Bruce, Vicki, Peter J. B. Hancock, and A. Mike Burton. "Human Face Perception and Identification." In Face Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_3.

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Viennet, Emmanuel, and Françoise Fogelman Soulié. "Connectionists Methods for Human Face Processing." In Face Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_7.

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Schubert, A., and E. D. Dickmanns. "Real-time Gaze Observation for Tracking Human Control of Attention." In Face Recognition. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_41.

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Uwechue, Okechukwu A., and Abhijit S. Pandya. "Face Recognition." In Human Face Recognition Using Third-Order Synthetic Neural Networks. Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-4092-2_2.

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Rahman, S. M. Mahbubur, Tamanna Howlader, and Dimitrios Hatzinakos. "Face Recognition." In Orthogonal Image Moments for Human-Centric Visual Pattern Recognition. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9945-0_3.

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Wallbott, Harald G. "Recognition of Emotion in Specific Populations: Compensation, Deficit or Specific (Dis)Abilities?" In The Human Face. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1063-5_9.

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Wang, Weihong, Jie Yang, Jianwei Xiao, Sheng Li, and Dixin Zhou. "Face Recognition Based on Deep Learning." In Human Centered Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15554-8_73.

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Johnston, Patrick, and Vaughan Carr. "Facial Affect Recognition Deficits in Schizophrenia: A Case for Applying Facial Measurement Techniques." In The Human Face. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-1063-5_6.

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Jiao, Yanbin, Jucheng Yang, Zhijun Fang, Shanjuan Xie, and Dongsun Park. "Comparing Studies of Learning Methods for Human Face Gender Recognition." In Biometric Recognition. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35136-5_9.

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Ren, Jianfeng, Xudong Jiang, and Junsong Yuan. "Face and Facial Expressions Recognition and Analysis." In Human–Computer Interaction Series. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19947-4_1.

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Conference papers on the topic "Human face recognition"

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"Human and face recognition." In 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2015. http://dx.doi.org/10.1109/ipta.2015.7367141.

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Owais, Muhammad, Ammara Shaikh, Aireen Amir Jalal, and Muhammad Moiz Hassan. "Human Face Recognition using PCA Eigenfaces." In 2019 IEEE 6th International Conference on Engineering Technologies and Applied Sciences (ICETAS). IEEE, 2019. http://dx.doi.org/10.1109/icetas48360.2019.9117489.

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Pike, G. "The psychology of human face recognition." In IEE Colloquium on Visual Biometrics. IEE, 2000. http://dx.doi.org/10.1049/ic:20000471.

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Ravindranath, Saurabh, Rahul Baburaj, Vineeth N. Balasubramanian, NageswaraRao Namburu, Sujit Gujar, and C. V. Jawahar. "Human-Machine Collaboration for Face Recognition." In CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD. ACM, 2020. http://dx.doi.org/10.1145/3371158.3371160.

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Chenggang Zhen and Yingmei Su. "Research about human face recognition technology." In 2009 International Conference on Test and Measurement (ICTM). IEEE, 2009. http://dx.doi.org/10.1109/ictm.2009.5412901.

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Liu, Ke, Frederic Jallut, Ying-Jiang Liu, Yong-Qing Cheng, and Jingyu Yang. "Novel approach to human face recognition." In San Diego '92, edited by Andrew G. Tescher. SPIE, 1993. http://dx.doi.org/10.1117/12.139107.

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Singh, Geetika, and Indu Chhabra. "Human face recognition through moment descriptors." In 2014 Recent Advances in Engineering and Computational Sciences (RAECS). IEEE, 2014. http://dx.doi.org/10.1109/raecs.2014.6799551.

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Kamgar-Parsi, Behrooz, Wallace Edgar Lawson, and Behzad Kamgar-Parsi. "Face recognition motivated by human approach." In SPIE Defense, Security, and Sensing, edited by B. V. K. Vijaya Kumar, Salil Prabhakar, and Arun A. Ross. SPIE, 2010. http://dx.doi.org/10.1117/12.855707.

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Cai, Defu, and Mingfang Zhang. "Model matching for human face recognition." In Intelligent Systems & Advanced Manufacturing, edited by David P. Casasent. SPIE, 1997. http://dx.doi.org/10.1117/12.290301.

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Singh Tomar, Vivek, Neetesh Gupta, and Upendra Singh. "Expressions Recognition Based on Human Face." In 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2019. http://dx.doi.org/10.1109/iccmc.2019.8819714.

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Reports on the topic "Human face recognition"

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Phillips, P. Jonathon, Alice J. O'Toole, Yi Cheng, Brendan Ross, and Heather A. Wild. Assessing algorithms as computational models for human face recognition. National Institute of Standards and Technology, 1999. http://dx.doi.org/10.6028/nist.ir.6348.

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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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Varastehpour, Soheil, Hamid Sharifzadeh, Iman Ardekani, and Abdolhossein Sarrafzadeh. Human Biometric Traits: A Systematic Review Focusing on Vascular Patterns. Unitec ePress, 2020. http://dx.doi.org/10.34074/ocds.086.

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Authentication methods based on human traits, including fingerprint, face, iris, and palm print, have developed significantly, and currently they are mature enough to be reliably considered for human identification purposes. Recently, as a new research area, a few methods based on non-facial skin features such as vein patterns have been developed. This literature review paper explores some key biometric systems such as face recognition, iris recognition, fingerprint, and palm print, and discusses their respective advantages and disadvantages; then by providing a comprehensive analysis of these

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Clarke, Alison, Sherry Hutchinson, and Ellen Weiss. Psychosocial support for children. Population Council, 2005. http://dx.doi.org/10.31899/hiv14.1003.

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Masiye Camp in Matopos National Park, and Kids’ Clubs in downtown Bulawayo, Zimbabwe, are examples of a growing number of programs in Africa and elsewhere that focus on the psychological and social needs of AIDS-affected children. Given the traumatic effects of grief, loss, and other hardships faced by these children, there is increasing recognition of the importance of programs to help them strengthen their social and emotional support systems. This Horizons Report describes findings from operations research in Zimbabwe and Rwanda that examines the psychosocial well-being of orphans and vulne

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Gurevitz, Michael, William A. Catterall, and Dalia Gordon. Learning from Nature How to Design Anti-insect Selective Pesticides - Clarification of the Interacting Face between Insecticidal Toxins and their Na-channel Receptors. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7697101.bard.

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Structural details on the interacting faces of toxins and sodium channels (Navs), and particularly identification of elements that confer specificity for insects, are difficult to approach and require suitable experimental systems. Therefore, natural toxins capable of differential recognition of insect and mammalian Navs are valuable leads for design of selective compounds in insect control. We have characterized several scorpion toxins that vary in preference for insect and mammalian Navs, and identified residues important for their action. However, despite many efforts worldwide, only little

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Kroll, Joshua A. ACM TechBrief: Facial Recognition Technology. ACM, 2022. http://dx.doi.org/10.1145/3520137.

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Facial recognition is not a monolithic technology or a particular technique. Rather, facial recognition refers to any technology that automatically processes and purports to identify faces in images or videos. While humans interpret faces easily, computers must extract patterns from data or humans must code patterns into the system. Applying these patterns yields the facial descriptors (often referred to as faceprints) on which facial recognition systems rely to achieve their function.

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Gordon, Dalia, Ke Dong, and Michael Gurevitz. Unexpected Specificity of a Sea Anemone Small Toxin for Insect Na-channels and its Synergic Effects with Various Insecticidal Ligands: A New Model to Mimic. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7697114.bard.

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Motivated by the high risks to the environment and human health imposed by the current overuse of chemical insecticides we offer an alternative approach for the design of highly active insect-selective compounds that will be based on the ability of natural toxins to differentiate between insect and mammalian targets. We wish to unravel the interacting surfaces of insect selective toxins with their receptor sites on voltage-gated sodium channels. In this proposal we put forward two recent observations that may expedite the development of a new generation of insect killers that mimic the highly

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Kukreja, Prateek, Havishaye Puri, and Dil Rahut. Creative India: Tapping the Full Potential. Asian Development Bank Institute, 2023. http://dx.doi.org/10.56506/kcbi3886.

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We provide the first reliable measure on the size of India’s creative economy, explore the many challenges faced by the creative industries, and provide recommendations to make India one of the most creative societies in the world. India’s creative economy—measured by the number of people working in various creative occupations—is estimated to contribute nearly 8% of the country’s employment, much higher than the corresponding share in Turkey (1%), Mexico (1.5%), the Republic of Korea (1.9%), and even Australia (2.1%). Creative occupations also pay reasonably well—88% higher than the non-creat

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Issues in Data Processing and Relevant Population Selection. OSAC Speaker Recognition Subcommittee, 2022. http://dx.doi.org/10.29325/osac.tg.0006.

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In Forensic Automatic Speaker Recognition (FASR), forensic examiners typically compare audio recordings of a speaker whose identity is in question with recordings of known speakers to assist investigators and triers of fact in a legal proceeding. The performance of automated speaker recognition (SR) systems used for this purpose depends largely on the characteristics of the speech samples being compared. Examiners must understand the requirements of specific systems in use as well as the audio characteristics that impact system performance. Mismatch conditions between the known and questioned

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