An Improved Face Recognition Algorithm Based on Sparse Representation

This paper considers a variation of Sparse Representation-based Classification algorithm. Accuracy and time of evaluation of face recognition are two key performance indicators. This work compares performance of modified Sparse Representation-based Classification algorithm against original Sparse Representation-based Classification algorithm. Yale Face Database B is used to carry MATLAB simulations and results show that modified Sparse Representation-based Classification algorithm outperforms in terms of time. Moreover, the authors study and compare these algorithms when there is only a few training samples per subject is available.

[1]  Cemil Turan Robust face recognition via sparse reconstruction vector , 2017, 2017 13th International Conference on Electronics, Computer and Computation (ICECCO).

[2]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[3]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[5]  Jian Yang,et al.  A Survey of Dictionary Learning Algorithms for Face Recognition , 2017, IEEE Access.

[6]  Rubén San-Segundo-Hernández,et al.  A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression , 2017, Artificial Intelligence Review.

[7]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Mohammad Shukri Salman,et al.  Illumination invariant face recognition system , 2013, 2013 21st Signal Processing and Communications Applications Conference (SIU).

[9]  Amjad Rehman,et al.  Face Recognition: A Survey , 2017 .