Unconstrained Facial Recognition Systems: A Review
Keywords:
Face Recognition, Deep Learning, Representation learning, feature learningAbstract
Face recognition presents a challenging problem in the field of image analysis and computer vision, and as such has received a great deal of attention over the last few years because of its applications in various domains. Face recognition under controlled environment that is where pose, illumination and other factors are controlled, has well been developed in the literature and near perfection accuracy results have been achieved. However, the unconstrained counterpart, where these factors are not controlled, still under heavy research. Â Recently, newly developed algorithms in the field that are based on deep learning technology have made significant progress. In this paper, an overview of the newly developed unconstrained facial recognition systems is presented.
Â
References
Facial recognition system, http://en.wikipedia.org/wiki/Facial_recognition_system
Yaniv Taigman and Lior Wolf, “Leveraging Billions of Faces to Overcome Performance Barriers in Unconstrained Face Recognitionâ€, Face.com, 2011.
Rabia Jafri and Hamid R. Arabnia, A Survey of Face Recognition Techniques, Journal of Information Processing Systems, Vol.5, No.2, June 2009
Cui Y, Fan L. Feature extraction using fuzzy maximum margin criterion[J]. Neurocomputing, 2012, 86: 52-58.
[10] Koç M, Barkana A. A new solution to one sample problem in face recognition using FLDA[J]. Applied Mathematics and Computation, 2011, 217(24): 10368-10376
Gao J, Fan L, Xu L. Median null (Sw)-based method for face feature recognition[J]. Applied Mathematics and Computation, 2013, 219(12): 6410-6419.
Gary B. Huang, Manu Ramesh, Tamara Berg, and Erik Learned-Miller, “Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environmentsâ€, http://vis-www.cs.umass.edu/lfw/lfw.pdf;
Zhao, W. & Chellappa, R. (2005). Face Processing: Advanced Modeling and Methods, Elsevier.
Zhao, W., Chellappa, R., Phillips, P. J. & Rosenfeld, A. (2003). Face recognition: A literature survey.
Robert D. Hof, “10 Breakthrough Technologies 2013: Deep Learningâ€, MIT Technology Review, http://www.technologyreview.com/featuredstory/513696/deep-learning/, April 23, 2013.
Yoshua Bengioy, Aaron Courville, and Pascal Vincen, “Representation Learning: A Review and New Perspectivesâ€, 2014.f[20].
Antonio Regalado, “Is Google Cornering the Market on Deep Learning?â€, MIT Technology Review, http://www.technologyreview.com/news/524026/is-google-cornering-the-market-on-deep-learning/, January 29, 2014.
Cade Metz, “Facebook Taps ‘Deep Learning’ Giant for New AI Labâ€, http://www.wired.com/2013/12/facebook-yann-lecun/; September 12,2013.
Divyarajsinh N. Parmar, Brijesh B. Mehta2, “Face Recognition Methods & Applicationsâ€, Divyarajsinh N Parmar et al ,Int.J.Computer Technology & Applications,Vol 4 (1),84-86, 2013;
Rabia Jafri and Hamid R. Arabnia, “A Survey of Face Recognition Techniquesâ€, Journal of Information Processing Systems, Vol.5, No.2, June 2009.
Trevor Hastie, Robert Tibshirani, Jerome Friedman, “The Elements of Statistical Learning: Data Mining, Inference, and Predictionâ€, Springer, 2008.
Li Deng and Dong Yu, “DEEP LEARNING: METHODS AND APPLICATIONSâ€, Microsoft Research, NOW PUBLISHERS, 2014.
Yann LeCun, Leon Bottou, Yoshua Benjio and Patrick Haffner, “Gradient-Based Learning Applied to Document Recognitionâ€, IEEE, November 1998.
Yann LeCun, Koray Kavukcuoglu and Cl´ement Farabet, “Convolutional Networks and Applications in Visionâ€, Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), June, 2010;
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networksâ€, Neural Information Processing Systems proceedings, 2012.
Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, Lior Wolf, “DeepFace: Closing the Gap to Human-Level Performance in Face Verificationâ€, facebook.com, June, 2014.
Jiwen Lu, Gang Wang, Weihong Deng, and Kui Jia, Reconstruction-Based Metric Learning for Unconstrained Face Verification, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 10, NO. 1, JANUARY 2015, pp. 79-89.
Downloads
Published
Issue
Section
License
Copyright © The Author(s). This article is published under the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
