Sparse Representation and Face Recognition

Full Text (PDF, 1393KB), PP.11-20

Views: 0 Downloads: 0


M. Khorasani 1,* Sedigheh Ghofrani 2 M. Hazari 3

1. Brain Engineering Research Center, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran

2. Electrical and Electronic Engineering Department, Islamic Azad University, South Tehran Branch, Tehran, Iran

3. Data Processing Research Center, Khajeh Nasir Toosi Research Center on Developing Advanced Technologies, Tehran, Iran.

* Corresponding author.


Received: 25 Jul. 2018 / Revised: 16 Aug. 2018 / Accepted: 17 Sep. 2018 / Published: 8 Dec. 2018

Index Terms

Sparse representation, compressive sensing, face recognition, recovery algorithm, OMP


Now a days application of sparse representation are widely spreading in many fields such as face recognition. For this usage, defining a dictionary and choosing a proper recovery algorithm plays an important role for the method accuracy. In this paper, two type of dictionaries based on input face images, the method named SRC, and input extracted features, the method named MKD-SRC, are constructed. SRC fails for partial face recognition whereas MKD-SRC overcomes the problem. Three extension of MKD-SRC are introduced and their performance for comparison are presented. For recommending proper recovery algorithm, in this paper, we focus on three greedy algorithms, called MP, OMP, CoSaMP and another called Homotopy. Three standard data sets named AR, Extended Yale-B and Essex University are used to asses which recovery algorithm has an efficient response for proposed methods. The preferred recovery algorithm was chosen based on achieved accuracy and run time.

Cite This Paper

M. Khorasani, S. Ghofrani, M. Hazari, " Sparse Representation and Face Recognition", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.12, pp. 11-20, 2018. DOI: 10.5815/ijigsp.2018.12.02


[1]C. C. Chen, Y. S. Shieh and H. T. Chu, "Face image retrieval by projection-based features," in International Workshop on Image Media Quality and Applications, Kioto, 2008.

[2]C. Zhang, H. Chen, M. Chen and Z. Sun, "Image matrix fisher discriminant analysis (IMFDA)-2D matrix based face image retrieval algorithm," in International Conference on Advances in Web-Age Information Management, Hangzhou, 2005.

[3]H. C. Kim, D. Kim and S. Y. Bang, "Face retrieval using 1st-and 2nd-order PCA mixture model," in International Conference on Image Processing, New York, 2002.

[4]T. Ojala, M. Pietikäinen and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognition, vol. 29, pp. 51-59, 1996.

[5]T. Ahonen, A. Hadid, and M. Pietikainen, "Face description with local binary patterns: Application to face recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 28, pp. 2037-2041, 2006.

[6]T. Ahonen, A. Hadid, and M. Pietikäinen, "Face recognition with local binary patterns," in  European Conference on Computer Vision, 2004.

[7]G. Zhang, X. Huang, S. Z. Li, Y Wang, and X. Wu, "Boosting local binary pattern (LBP)-based face recognition," in Advances in Biometric Person Authentication, 2004.

[8]S. Li, R. Chu, M. Ao, L. Zhang, and R. H, "Highly accurate and fast face recognition using near infrared images," in International Conference on Advances in Biometrics, 2006.

[9]W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, "Local gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition," in Tenth IEEE International Conference on Computer Vision (ICCV'05) , 2005.

[10]J. Wright, A. Yang, A. Ganesh, S. Sastry, "Robust face recognition via sparse representation," IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 31, no. 2, pp. 210-227, 2009.

[11]H. Li, P. Wang and C. Shen, "Robust face recognition via accurate face alignment and sparse representation," in International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2010.

[12]A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi and Y. Ma, "Toward a practical face recognition system: robust alignment and illumination by sparse representation," IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 34, no. 2, pp. 372-386, 2012.

[13]W. Ou, P. Zhanga, Y. Tanga and Z. Zhua, "Robust face recognition via occlusion dictionary learning," Pattern Recognition, vol. 47, no. 4, pp. 1559–1572, 2014.

[14]H. Zhang,, N. M. Nasrabadi, Y. Zhang, and T. S. Huang, "Joint dynamic sparse representation for multi-view face recognition," Pattern Recognition, vol. 45, no. 5, pp. 1292-1298, 2012.

[15]H. Zhang, Y.Zhang and T.S. Huang, "Pose-robust face recognition via sparse representation," Pattern Recognition, vol. 46, no. 5, pp. 1511-1521, 2013.

[16]S. Liao, A. K. Jain, and S. Z. Li, "Partial face recognition:alignment-free approach," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 5, pp. 1193-1205, 2013.

[17]P. Vaidyanathan, "Generalizations of sampling theorem; seven dacades after nyquist," IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol. 48, no. 9, pp. 1094-1109, 2001.

[18]S Mallat, Z Zhang. , "Matching pursuit in a timefrequency dictionary," IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3397-3415, 1993.

[19]L. Du, R. Wang, W. Wan, X. Yu, "Analysis on greedy reconstruction algorithms based on compressed sensing," in International Conference on Audio, Language and Image Processing (ICALIP), 2012.

[20]M. Asif, J. Romberg, "Sparse recovery of streaming signals using l1-Homotopy," IEEE Transactions on Signal Processing, vol. 62, no. 16, pp. 4209-4223, 2014.

[21]R. Baraniuk, "Compressive sensing," IEEE Signal Processing Magazine, 2007.

[22]E. Candes, J. Romberg, T. Tao, "Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information," IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489-509, 2006.

[23]D. Donoho, "Compressed sensing," IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289-1306, 2006.

[24]X. Tan and B. Triggs, "Enhanced local texture feature sets for face recognition under dif´Čücult lighting conditions," IEEE Transactions on Image Processing, vol. 19, no. 6, pp. 1635-1650, 2007.

[25]"Scale & affine invariant feature detectors," 2012. [Online]. Available:

[26]M. Martinez, "The Ohio State University," 24 Jun 1998. [Online]. Available:

[27]Kuang-Chih Lee, Jeffrey Ho, and David Kriegman, "The extended Yale face database B," [Online]

[28]L. Spacek, "University of Essex," 20 Jun 2008 . [Online]. Available:

[29]S. Ghofrani, R. Alikiaamiri, M. Khorasani, "Comparing the performance of recovery algorithms for robust face recognition," in CGVCVIP 2015 Proceedings, 2015.