Bibliografías temáticas / Face recognition

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Literatura académica sobre el tema "Face recognition"

Autor: Grafiati
Publicado: 4 de junio de 2021
Última modificación: 9 de junio de 2025

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Artículos de revistas sobre el tema "Face recognition"

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Deshmukh, Sagar, Sanjay Rawat, and Shubhangi Patil. "Face Recognition Technology." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1612–13. http://dx.doi.org/10.31142/ijtsrd14331.

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Yadav, Rakeshkumar H., Brajgopal Agarwal, and Sheeba James. "Face Recognition System." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 1815–18. http://dx.doi.org/10.31142/ijtsrd14453.

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Ounachad, Khalid, Mohamed Oualla, Abdelalim Sadiq, and Abdelghani Sohar. "Face Sketch Recognition: Gender Classification and Recognition." International Journal of Psychosocial Rehabilitation 24, no. 03 (February 18, 2020): 1073–85. http://dx.doi.org/10.37200/ijpr/v24i3/pr200860.

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V, Prathama, and Thippeswamy G. "Age Invariant Face Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (June 30, 2019): 971–76. http://dx.doi.org/10.31142/ijtsrd23572.

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Lakshmi, Dr D. Vijaya, Srija Reddy Ardha, and Anand karthik Azmeera. "Cross Age Face Recognition." International Journal of Research Publication and Reviews 6, no. 4 (April 2025): 4381–407. https://doi.org/10.55248/gengpi.6.0425.1466.

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Rakeshkumar, H. Yadav, Agarwal Brajgopal, and James Sheeba. "Face Recognition System." International Journal of Trend in Scientific Research and Development 2, no. 4 (June 21, 2018): 1815–18. https://doi.org/10.31142/ijtsrd14453.

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Face is a important part through which we can identify who we are and how people identify us. Face is a persons most valuable and unique physical characteristics through which we can identify someone very easily. While humans have the innate ability to distinguish the different faces for millions of years for adding the new technology computers are just now catching up. A face recognition system is a computer application that is capable of identifying or verifying the person from a digital image or a video frame from video source. One of the way is to do this is by compare with the selected facial features and also a face database. Humans are having good tendency to recognizing faces. Rakeshkumar H Yadav | Brajgopal Agarwal | Sheeba James "Face Recognition System" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: https://www.ijtsrd.com/papers/ijtsrd14453.pdf
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Telugu, Maddileti, Shriphad Rao G., Sai Madhav Vaddemani, and Sharan Ganti. "Home Security using Face Recognition Technology." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 2 (December 30, 2019): 678–82. https://doi.org/10.35940/ijeat.B3917.129219.

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Face is the easiest way to penetrate each other's personal identity. Face recognition is a method of personal identification using the personal characteristics of an individual to decide the identification of a person. The method of human face recognition consists basically of two levels, namely face detection and face recognition. There are three types of methods that are currently popular in the developed face recognition pattern, those are Eigen faces algorithm, Fisher faces algorithm and CNN neural network for face recognition
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Patel, Ibrahim, Raghavendra Kulkarni, and Dr P. Nageswar Rao. "Robust Singular Value Decomposition Algorithm for Unique Faces." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, no. 2 (June 21, 2018): 596–603. http://dx.doi.org/10.24297/ijct.v4i2c1.4178.

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It has been read and also seen by physical encounters that there found to be seven near resembling humans by appearance .Many a times one becomes confused with respect to identification of such near resembling faces when one encounters them. The recognition of familiar faces plays a fundamental role in our social interactions. Humans are able to identify reliably a large number of faces and psychologists are interested in understanding the perceptual and cognitive mechanisms at the base of the face recognition process. As it is needed that an automated face recognition system should be faces specific, it should effectively use features that discriminate a face from others by preferably amplifying distinctive characteristics of face. Face recognition has drawn wide attention from researchers in areas of machine learning, computer vision, pattern recognition, neural networks, access control, information security, law enforcement and surveillance, smart cards etc. The paper shows that the most resembling faces can be recognized by having a unique value per face under different variations. Certain image transformations, such as intensity negation, strange viewpoint changes, and changes in lighting direction can severely disrupt human face recognition. It has been said again and again by research scholars that SVD algorithm is not good enough to classify faces under large variations but this paper proves that the SVD algorithm is most robust algorithm and can be proved effective in identifying faces under large variations as applicable to unique faces. This paper works on these aspects and tries to recognize the unique faces by applying optimized SVD algorithm.
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Reddy, Mr B. Ravinder, V. Akhil, and G. Sai Preetham P. Sai Poojitha. "Profile Identification through Face Recognition." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (April 30, 2019): 1482–83. http://dx.doi.org/10.31142/ijtsrd23439.

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A, ASLIM S., and Dr A. MYTHILI. "Automation Attendance Using Face Recognition." International Journal of Research Publication and Reviews 6, no. 4 (April 2025): 257–60. https://doi.org/10.55248/gengpi.6.0425.1314.

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Tesis sobre el tema "Face recognition"

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Hanafi, Marsyita. "Face recognition from face signatures." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/10566.

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This thesis presents techniques for detecting and recognizing faces under various imaging conditions. In particular, it presents a system that combines several methods for face detection and recognition. Initially, the faces in the images are located using the Viola-Jones method and each detected face is represented by a subimage. Then, an eye and mouth detection method is used to identify the coordinates of the eyes and mouth, which are then used to update the subimages so that the subimages contain only the face area. After that, a method based on Bayesian estimation and a fuzzy membership function is used to identify the actual faces on both subimages (obtained from the first and second steps). Then, a face similarity measure is used to locate the oval shape of a face in both subimages. The similarity measures between the two faces are compared and the one with the highest value is selected. In the recognition task, the Trace transform method is used to extract the face signatures from the oval shape face. These signatures are evaluated using the BANCA and FERET databases in authentication tasks. Here, the signatures with discriminating ability are selected and were used to construct a classifier. However, the classifier was shown to be a weak classifier. This problem is tackled by constructing a boosted assembly of classifiers developed by a Gentle Adaboost algorithm. The proposed methodologies are evaluated using a family album database.
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Zhou, Shaohua. "Unconstrained face recognition." College Park, Md. : University of Maryland, 2004. http://hdl.handle.net/1903/1800.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2004.<br>Thesis research directed by: Electrical Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Ustun, Bulend. "3d Face Recognition." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12609075/index.pdf.

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In this thesis, the effect of registration process is evaluated as well as several methods proposed for 3D face recognition. Input faces are in point cloud form and have noises due to the nature of scanner technologies. These inputs are noise filtered and smoothed before registration step. In order to register the faces an average face model is obtained from all the images in the database. All the faces are registered to the average model and stored to the database. Registration is performed by using a rigid registration technique called ICP (Iterative Closest Point), probably the most popular technique for registering two 3D shapes. Furthermore some variants of ICP are implemented and they are evaluated in terms of accuracy, time and number of iterations needed for convergence. At the recognition step, several recognition methods, namely Eigenface, Fisherface, NMF (Nonnegative Matrix Factorization) and ICA (Independent Component Analysis) are tested on registered and non-registered faces and the performances are evaluated.
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Wong, Vincent. "Human face recognition /." Online version of thesis, 1994. http://hdl.handle.net/1850/11882.

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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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Qu, Yawe, and Mingxi Yang. "Online Face Recognition Game." Thesis, Halmstad University, School of Information Science, Computer and Electrical Engineering (IDE), 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-248.

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<p>The purpose of this project is to test and improve people’s ability of face recognition. </p><p>Although there are some tests on the internet with the same purpose, the problem is that people </p><p>may feel bored and give up before finishing the tests. Consequently they may not benefit from </p><p>testing nor from training. To solve this problem, face recognition and online game are put </p><p>together in this project. The game is supposed to provide entertainment when people are playing, </p><p>so that more people can take the test and improve their abilities of face recognition. </p><p>In the game design, the game is assumed to take place in the face recognition lab, which is </p><p>an imaginary lab. The player plays the main role in this game and asked to solve a number of </p><p>problems. There are several scenarios waiting for the player, which mainly need face recognition </p><p>skills from the player. At the end the player obtains the result of evaluation of her/his skills in </p><p>face recognition.</p>
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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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Graham, Daniel B. "Pose-varying face recognition." Thesis, University of Manchester, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.488288.

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Zhou, Mian. "Gobor-boosting face recognition." Thesis, University of Reading, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.494814.

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In the past decade, automatic face recognition has received much attention by both the commercial and public sectors as an efficient and resilient recognition technique in biometrics. This thesis describes a highly accurate appearance-based algorithm for grey scale front-view face recognition - Gabor-Boosting face recognition by means of computer vision, pattern recognition, image processing, machine learning etc. The strong performance of the Gabor-boosting face recognition algorithm is highlighted by combining three key leading edge techniques - the Gabor wavelet transform, AdaBoost, Support Vector Machine (SVM). The Gabor wavelet transform is used to extract features which describe texture variations of human faces. The Adaboost algorithm is used to select most significant features which represent different individuals. The SVM constructs a classifier with high recognition accuracy. Within the AdaBoost algorithm, a novel weak learner - Potsu is designed. The Potsu weak learner is fast due to the simple perception prototype, and is accurate due to large number of training examples available. More importantly, the Potsu weak learner is the only weak learner which satisfies the requirement of AdaBoost. The Potsu weak learners also demonstrate superior performance over other weak learners, such as FLD. The Gabor-Boosting face recognition algorithm is extended into multi-class classification domain, in which a multi-class weak learner called mPotsu is developed. The experiments show that performance is improved by applying loosely controlled face recognition in the multi-class classification.
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Abi, Antoun Ramzi. "Pose-Tolerant Face Recognition." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/244.

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Automatic face recognition performance has been steadily improving over years of active research, however it remains significantly affected by a number of external factors such as illumination, pose, expression, occlusion and resolution that can severely alter the appearance of a face and negatively impact recognition scores. The focus of this thesis is the pose problem which remains largely overlooked in most real-world applications. Specifically, we focus on one-to-one matching scenarios where a query face image of a random pose is matched against a set of “mugshot-style” near-frontal gallery images. We argue that in this scenario, a 3D face-modeling geometric approach is essential in tackling the pose problem. For this purpose, we utilize a recent technique for efficient synthesis of 3D face models called 3D General Elastic Model (3DGEM). It solved the pose synthesis problem from a single frontal image, but could not solve the pose correction problem because of missing face data due to self-occlusion. In this thesis, we extend the formulation of 3DGEM and cast this task as an occlusion-removal problem. We propose a sparse feature extraction approach using subspace-modeling and `1-minimization to find a representation of the geometrically 3D-corrected faces that we show is stable under varying pose and resolution. We then show how pose-tolerance can be achieved either in the feature space or in the reconstructed image space. We present two different algorithms that capitalize on the robustness of the sparse feature extracted from the pose-corrected faces to achieve high matching rates that are minimally impacted by the variation in pose. We also demonstrate high verification rates upon matching nonfrontal to non-frontal faces. Furthermore, we show that our pose-correction framework lends itself very conveniently to the task of super-resolution. By building a multiresolution subspace, we apply the same sparse feature extraction technique to achieve single-image superresolution with high magnification rates. We discuss how our layered framework can potentially solve both pose and resolution problems in a unified and systematic approach. The modularity of our framework also keeps it flexible, upgradable and expandable to handle other external factors such as illumination or expressions. We run extensive tests on the MPIE dataset to validate our findings.
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Libros sobre el tema "Face recognition"

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Wechsler, Harry, P. Jonathon Phillips, Vicki Bruce, Françoise Fogelman Soulié, and Thomas S. Huang, eds. Face Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1.

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Vicki, Bruce, ed. Face recognition. Hove, U.K: Lawrence Erlbaum Associates, 1991.

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Berle, Ian. Face Recognition Technology. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36887-6.

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Li, Stan Z., and Anil K. Jain, eds. Handbook of Face Recognition. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-932-1.

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Wechsler, Harry. Reliable Face Recognition Methods. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-38464-1.

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Li, Stan Z., Anil K. Jain, and Jiankang Deng, eds. Handbook of Face Recognition. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-43567-6.

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Elms, Natalie M. Other-race faces: Limitations of expert face recognition. St. Catharines, Ont: Brock University, Dept. of Psychology, 2007.

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Mou, Dengpan. Machine-based Intelligent Face Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-00751-4.

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Bate, Sarah. Face Recognition and its Disorders. London: Macmillan Education UK, 2013. http://dx.doi.org/10.1007/978-1-137-29277-3.

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Bourlai, Thirimachos, ed. Face Recognition Across the Imaging Spectrum. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28501-6.

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Capítulos de libros sobre el tema "Face recognition"

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Biederman, Irving, and Peter Kalocsai. "Neural and Psychophysical Analysis of Object and Face Recognition." In Face Recognition, 3–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_1.

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Okada, Kazunori, Johannes Steffens, Thomas Maurer, Hai Hong, Egor Elagin, Hartmut Neven, and Christoph von der Malsburg. "The Bochum/USC Face Recognition System and How it Fared in the FERET Phase III Test." In Face Recognition, 186–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_10.

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Nastar, Chahab. "Face Recognition Using Deformable Matching." In Face Recognition, 206–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_11.

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Moghaddam, Baback, and Alex Pentland. "Beyond Linear Eigenspaces: Bayesian Matching for Face Recognition." In Face Recognition, 230–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_12.

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Phillips, P. Jonathon, Hyeonjoon Moon, Syed Rizvi, and Patrick Rauss. "The FERET Evaluation." In Face Recognition, 244–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_13.

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Tistarelli, Massimo, and Enrico Grosso. "Active Vision-based Face Recognition: Issues, Applications and Techniques." In Face Recognition, 262–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_14.

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Proesmans, Marc, and Luc Van Gool. "Getting Facial Features and Gestures in 3D." In Face Recognition, 287–309. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_15.

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Vetter, Thomas, and Volker Blanz. "Generalization to Novel Views from a Single Face Image." In Face Recognition, 310–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_16.

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Gutta, Srinivas, and Harry Wechsler. "Modular Forensic Architectures." In Face Recognition, 327–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_17.

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Huang, Jeffrey, Chengjun Liu, and Harry Wechsler. "Eye Detection and Face Recognition Using Evolutionary Computation." In Face Recognition, 348–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_18.

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Actas de conferencias sobre el tema "Face recognition"

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J, Anil, L. Padma Suresh, P. Muthukumar, S. H. Krishna Veni, P. Asha, and Rajesh Prasad. "Literature Survey on Face Recognition of Occluded Faces." In 2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT), 1930–37. IEEE, 2024. http://dx.doi.org/10.1109/iccpct61902.2024.10672761.

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Vats, Janvi, Divyanshu Kumar Singh, and Kavita Saini. "Face Recognition Attendance System." In 2024 1st International Conference on Advances in Computing, Communication and Networking (ICAC2N), 260–64. IEEE, 2024. https://doi.org/10.1109/icac2n63387.2024.10895051.

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Alskeini, Neamah H., Kien Nguyen Thanh, Vinod Chandran, and Wageeh Boles. "Face recognition." In the 2nd International Conference. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3282286.3282291.

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Xiangyun Qing and Xingyu Wang. "Face Recognition using Laplacian+OPRA-faces." In 2006 6th World Congress on Intelligent Control and Automation. IEEE, 2006. http://dx.doi.org/10.1109/wcica.2006.1713957.

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Plyakin, Vladislav, and Vladislav Protasov. "Evolutionary matching method for face recognition using neural networks." In International Conference "Computing for Physics and Technology - CPT2020". ANO «Scientific and Research Center for Information in Physics and Technique», 2020. http://dx.doi.org/10.30987/conferencearticle_5fd755bf868b47.13424079.

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The problem of formalizing and automating the process of recognizing human faces was touched upon at the earliest stages of the development of image recognition systems and remains relevant to this day. Moreover, over the past ten years, the number of scientific studies and publications on this topic has increased several times, which indicates an increase in the urgency of this problem. This can be explained by the fact that modern computing technology opens up new possibilities for its application in various fields, and, accordingly, a lot of applied problems have appeared that require their speedy resolution. One of the practical applications of the pattern recognition theory is face recognition, the task of which is to automatically localize a face in an image and identify a person by face. The interest in the procedures underlying the process of localization and face recognition is quite significant due to the variety of their practical applications in areas such as security systems, verification, forensic examination, teleconferences, computer games, etc. For example, the face recognition system developed at Beijing Tsinghua University has been certified by the Chinese Ministry of Public Security for use in public places. Omron Japan, which specializes in recognition, automation and control technologies, has developed a human face recognition system for mobile phones. Riya, founded by a group of specialists in facial recognition algorithms from Stanford University, has begun open testing of a Web service for contextual search of facial images in digital photo albums. The abundance of such examples indicates the practical importance and relevance of face recognition methods.
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Abdulkareem Saber, Alla, and Gullanar M Hadi. "Face Recognition Using Convolutional Neural Network." In 5TH INTERNATIONAL CONFERENCE ON COMMUNICATION ENGINEERING AND COMPUTER SCIENCE (CIC-COCOS'24). Cihan University-Erbil, 2024. http://dx.doi.org/10.24086/cocos2024/paper.1553.

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One of the important areas of computer vision is the face recognition (FR) issue. FR is used to detect faces seen on dispersed cameras throughout a network. There are two categories of face recognition problems. The first category, called the face identification issue involves recognizing faces based on samples, per person. One method of face recognition known as Single Sample Per Person (SSPP) is the type commonly referred to as "current face recognition approaches." There is research that supports both methods and deep learning techniques. This research paper introduces a CNN model that aims to expedite network convergence and minimize the duration of training. As a step we incorporate a normalization layer, into the CNN framework. To accommodate the normalized data, we include an activation function, within the existing activation function allowing for modifications. Finally, we utilize max pooling to ensure that the feature information remains stable and intact while still preserving its details. The experimental findings demonstrate that our approach outperforms the leading technique and addresses the issue of results, between the training and test datasets resulting in a lower rate of recognition.
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Chaudhari, S. T., and A. Kale. "Face Normalization: Enhancing Face Recognition." In Third International Conference on Emerging Trends in Engineering and Technology (ICETET 2010). IEEE, 2010. http://dx.doi.org/10.1109/icetet.2010.83.

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Hassner, Tal, Iacopo Masi, Jungyeon Kim, Jongmoo Choi, Shai Harel, Prem Natarajan, and Gerard Medioni. "Pooling Faces: Template Based Face Recognition with Pooled Face Images." In 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2016. http://dx.doi.org/10.1109/cvprw.2016.23.

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Patel, Maitrey, Abhishek Nath Goswami, Lokesh Mishra, Prabhat Tripathi, and Vivek Rai. "AUTOMATIC PAYMENT USING FACE RECOGNITION SYSTEM." In Computing for Sustainable Innovation: Shaping Tomorrow’s World. Innovative Research Publication, 2024. http://dx.doi.org/10.55524/csistw.2024.12.1.9.

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This abstract introduces an innovative system for automatic payments using face recognition, eliminating the need for traditional payment cards. In this proposed system, users authenticate transactions by presenting their faces to the recognition system, prioritizing Face ID for its effectiveness in identification. The technology relies on advanced biometric techniques, including OpenCV for image processing, Haar Cascade Classifier for face detection, and Local Binary Pattern for facial recognition. Upon successful face verification, the payment is automatically processed, streamlining transactions, and eliminating the necessity for physical payment cards and PINs. The system continually refines its accuracy through model training based on successful face recognition instances. This forward-looking approach not only enhances security but also provides a convenient and efficient alternative to traditional payment methods, offering a glimpse into the future of seamless and secure financial transactions.
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Zhao, Ming, and Tat-Seng Chua. "Markovian mixture face recognition with discriminative face alignment." In Gesture Recognition (FG). IEEE, 2008. http://dx.doi.org/10.1109/afgr.2008.4813443.

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Informes sobre el tema "Face recognition"

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Socolinsky, Diego A., and Andrea Selinger. Thermal Face Recognition Over Time. Fort Belvoir, VA: Defense Technical Information Center, January 2006. http://dx.doi.org/10.21236/ada444423.

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Beymer, David J. Face Recognition Under Varying Pose. Fort Belvoir, VA: Defense Technical Information Center, December 1993. http://dx.doi.org/10.21236/ada290205.

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Phillips, P. Jonathon, Patrick Grother, Ross J. Micheals, Duane M. Blackburn, Elham Tabassi, and Mike Bone. Face recognition vendor test 2002 :. Gaithersburg, MD: National Institute of Standards and Technology, 2003. http://dx.doi.org/10.6028/nist.ir.6965.

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Grother, Patrick. Face recognition vendor test 2002 :. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7083.

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Ngan, M., and P. Grother. Face Recognition Vendor Test (FRVT) :. Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.7995.

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Grother, Patrick, and Mei Ngan. Face Recognition Vendor Test (FRVT). Gaithersburg, MD: National Institute of Standards and Technology, 2014. http://dx.doi.org/10.6028/nist.ir.8009.

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Ngan, Mei, Patrick Grother, and Kayee Hanaoka. Ongoing Face Recognition Vendor Test (FRVT) Part 6B: Face recognition accuracy with face masks using post-COVID-19 algorithms. National Institute of Standards and Technology, November 2020. http://dx.doi.org/10.6028/nist.ir.8331.

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Phillips, P. Jonathon, and Elaine M. Newton. Meta-analysis of face recognition algorithms. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6719.

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Phillips, P. Jonathon, Patrick J. Flynn, Todd Scruggs, Kevin W. Bowyer, and William Worek. Preliminary face recognition grand challange results. Gaithersburg, MD: National Institute of Standards and Technology, 2006. http://dx.doi.org/10.6028/nist.ir.7307.

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Grother, Patrick, Mei Ngan, and Kayee Hanaoka. Face recognition vendor test part 3:. Gaithersburg, MD: National Institute of Standards and Technology, December 2019. http://dx.doi.org/10.6028/nist.ir.8280.

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