Email this article 
Open Access
Subscription or Fee Access
Face Recognition Using Sparse Representation Classifier
Abstract
Face is a biometric. Face recognition finds wide application in the identification, security, safety, etc. but the natural use of face recognition technology is the replacement of security password. The previous algorithms used for recognition purpose like Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Support Vector Machine (SVM) has a problem like few feature extraction, poor accuracy result and so on. Hence, these algorithms are not 100% accurate for the face recognition purpose. So, because of this problem we obtain a new approach called Sparse Representation Classifier (SRC) for the identification of face. In the training phase, when we are applying a face to the detector, for each face a grid of patches is find out from each face in order to obtain a database for the recognition. After that, in the testing phase a grid is extracted from the input image and patch of every image is converted in to a binary representation using the database and after that they creating extraction of the face. Sparse representation classifier is deals with large degree of variations in lighting, person’s movements and expressions. It also deals with different face size.
Keywords
References
Naja M. I., Afzal A. L., Fingerprint Compression Based on Sparse representation, IEEE Transactions signal process ,vol. 41, no. 12,pp. 3397-3415,Dec.2008.
X. Wei,C.-T. Li, Z. Lei, D. Yi, and S. Li, “Dynamic Image-to-Class Warping for Occluded Face Recognition,” IEEE Transactions on Information Forensics and Security, vol. 9, no. 12, pp. 2035–2050, Dec 2014.
P. J. Phillips, J. R. Beveridge, B. A. Draper, G. Givens, A. J. O Toole, D. S. Bolme, J. Dunlop, Y. M. Lui, H. Sahibzada, and S. Weimer, “An introduction to the good, the bad, & the ugly face recognition challenge problem,” in 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG). IEEE, 2011, pp. 346–353.
Y. Taigman, M. Yang, M. Ranzato, and L. Wolf, “Deepface: Closing the gap to human-level performance in face verification, in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014, pp.1701–1708.
J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009.
A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, “Toward a practical face recognition system: Robust alignment illumination by sparse representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 2, pp. 372–386, 2012
W. Deng, J. Hu, and J. Guo, “Extended SRC: Undersampled face recognition via intraclass variant dictionary,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1864– 1870, 2012.
J. Wang, C. Lu, M. Wang, P. Li, S. Yan, and X. Hu, Robust face recognition via adaptive sparse representation, IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2368–2378, Dec 2014.
K. Jia, T.-H. Chan, and Y. Ma, Robust and practical face recognition via structured sparsity, in Computer Vision ECCV 2012. Springer, 2012, pp. 331–344.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.