Relevant bibliographies by topics / Face recognition

Contents

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

Academic literature on the topic 'Face recognition'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "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 (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 (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 (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 (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 (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 (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 fa
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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 (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 (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 fa
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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 (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 (2025): 257–60. https://doi.org/10.55248/gengpi.6.0425.1314.

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Dissertations / Theses on the topic "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 f
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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
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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>
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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
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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 galler
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Books on the topic "Face recognition"

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

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Vicki, Bruce, ed. Face recognition. Lawrence Erlbaum Associates, 1991.

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Berle, Ian. Face Recognition Technology. 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. Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-932-1.

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Wechsler, Harry. Reliable Face Recognition Methods. 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. 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. Brock University, Dept. of Psychology, 2007.

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Mou, Dengpan. Machine-based Intelligent Face Recognition. 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. 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. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28501-6.

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Book chapters on the topic "Face recognition"

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Biederman, Irving, and Peter Kalocsai. "Neural and Psychophysical Analysis of Object and Face Recognition." In Face Recognition. 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, et al. "The Bochum/USC Face Recognition System and How it Fared in the FERET Phase III Test." In Face Recognition. 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. 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. 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. 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. 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. 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. 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. 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. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-72201-1_18.

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Conference papers on the topic "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). 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). 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. 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
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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
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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, et al. "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 transac
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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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Reports on the topic "Face recognition"

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

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Beymer, David J. Face Recognition Under Varying Pose. Defense Technical Information Center, 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 :. 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 :. 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) :. 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). 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, 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. 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. 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:. National Institute of Standards and Technology, 2019. http://dx.doi.org/10.6028/nist.ir.8280.

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