SPARSE REPRESENTATION THEORY AND ITS APPLICATION FOR FACE RECOGNITION

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

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Subject: Computational Science & Engineering , Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 1 (March 2015) > List of articles

SPARSE REPRESENTATION THEORY AND ITS APPLICATION FOR FACE RECOGNITION

Yongjiao Wang / Chuan Wang / Lei Liang

Keywords : Face recognition, recognition rate, non-ideal imaging environments, sparse representation

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 1, Pages 107-124, DOI: https://doi.org/10.21307/ijssis-2017-751

License : (CC BY-NC-ND 4.0)

Received Date : 30-October-2014 / Accepted: 07-January-2015 / Published Online: 01-March-2015

ARTICLE

ABSTRACT

Face recognition aims at endowing computers with the ability to identify different human beings according to their face images. However, recognition rate will decrease sharply when it refers to the non-ideal imaging environments or the incorporation of users, such as illumination, pose, expression variations and so on. Besides, it will be also influence the recognition results when the database is too large or small. Sparse representation based classification for face images has been one of efficient approaches for face recognition in recent years. Discrimination performance by using the sparse representation can also be applied to the face recognition, and any test sample can be expressed
as a linear span of the all training samples. Experimental results show that face recognition method based on sparse representation is comparable to others.

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REFERENCES

[1] Zhao W, Krishnaswamy A, Chellappa R, et al. Discriminant analysis of principal components for face
recognition[M]. Face Recognition. Springer Berlin Heidelberg, 1998: 73-85.
[2] Yang J, Zhang D, Frangi A F, et al. Two-dimensional PCA: a new approach to appearance-based face
representation and recognition, Pattern Analysis and Machine Intelligence, IEEE Transactions on,
2004, 26(1): 131-137.
[3] Tan X, Triggs B. Enhanced local texture feature sets for face recognition under difficult lighting
conditions, Image Processing, IEEE Transactions on, 2010, 19(6): 1635-1650.
[4] Shakhnarovich G, Moghaddam B. Face recognition in subspaces, Handbook of Face Recognition.
Springer London, 2011: 19-49.
[5] Sadeghi A R, Schneider T, Wehrenberg I. Efficient privacy-preserving face recognition, Information,
Security and Cryptology–ICISC 2009. Springer Berlin Heidelberg, 2010: 229-244.
[6] Yu L, He Z, Cao Q. Gabor texture representation method for face recognition using the Gamma and
generalized Gaussian models, Image and Vision Computing, 2010, 28(1): 177-187.
[7] Kyungim Baek , Bruce A. Draper , J. Ross Beveridge , Kai She. PCA vs. ICA: a comparison on the
FERET data set [C]. In Proc. Of the 4th International Conference on Computer Vision, 2002.
[8] Fang S Y, Fang J J. Automatic head and facial feature extraction based on geometry variations,
Computer-Aided Design, 2011, 43(12): 1729-1739.
[9] Moghaddam B, pentland A. Probabilistic visual learning for object representation [J]. IEEE Trans. on
Pattern Analysis and Machine Intelligence, 1997(7).
[10] M. Turk, A. Pentland. Eigenfaces for Recognition, Journal of Cognitive Neuroscience. vol.3, no.1,
pp.71-86, 1991.
[11] Marian Stewart Bartlett, Javier R. Movellan, and Terrence J. Sejnowski. Face recognition by
independent Component analysis, IEEE Trans. on Neural Networks, vol.13, no.6, 2002(12).
[12] Shakhnarovich G, Moghaddam B. Face recognition in subspaces, Handbook of Face Recognition.
Springer London, 2011: 19-49.
[13] Li Z, Lin D, Tang X. Nonparametric discriminant analysis for face recognition, Pattern Analysis and
Machine Intelligence, IEEE Transactions on, 2009, 31(4): 755-761.
[14] Shu X, Gao Y, Lu H. Efficient linear discriminant analysis with locality preserving for face
recognition. Pattern Recognition, 2012, 45(5): 1892-1898.
[15] Bekios-Calfa J, Buenaposada J M, Baumela L. Revisiting linear discriminant techniques in gender
recognition. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 2011, 33(4): 858-864.
[16] G. Sen Gupta, S.C. Mukhopadhyay, Michael Sutherland and Serge Demidenko, Wireless Sensor
Network for Selective Activity Monitoring in a home for the Elderly, Proceedings of 2007 IEEE
IMTC conference, Warsaw, Poland, (6 pages).
[17] Nikitidis S, Tefas A, Nikolaidis N, et al. Facial expression recognition using clustering discriminant
Non-negative Matrix Factorization, Image Processing (ICIP), 2011 18th IEEE International
Conference on. IEEE, 2011: 3001-3004.
[18] M.Iwahara, S.C.Mukhopadhyay, S.Yamada and F.P.Dawson, "Development of Passive Fault Current
Limiter in Parallel Biasing Mode", IEEE Transactions on Magnetics, Vol. 35, No. 5, pp 3523-3525,
September 1999.
[19] Wen Y, He L, Shi P. Face recognition using difference vector plus KPCA , Digital Signal Processing,
2012, 22(1): 140-146.
[20] Liu Q, Huang R, Lu H, et al. Face recognition using kernel-based fisher discriminant analysis,
Automatic Face and Gesture Recognition, 2002. Proceedings. Fifth IEEE International Conference on.
IEEE, 2002: 197-201.
[21] Candes E J, Romberg J K, Tao T. Stable signal recovery from incomplete and inaccurate
measurements, Communications on pure and applied mathematics, 2006, 59(8): 1207-1223.
[22] N.K.Suryadevara, A. Gaddam, R.K.Rayudu and S.C. Mukhopadhyay, “Wireless Sensors Network
based safe Home to care Elderly People: Behaviour Detection”, Sens. Actuators A: Phys. (2012),
doi:10.1016/j.sna.2012.03.020, Volume 186, 2012, pp. 277 – 283.
[23] Wright J, Yang A Y, Ganesh A, et al. Robust face recognition via sparse representation, Pattern
Analysis and Machine Intelligence, IEEE Transactions on, 2009, 31(2): 210-227.
[24] Donoho D L. For most large underdetermined systems of linear equations the minimal ℓ1‐norm
solution is also the sparsest solution, Communications on pure and applied mathematics, 2006, 59(6):
797-829.
[25] DeVore R A, Temlyakov V N. Some remarks on greedy algorithms, Advances in computational
Mathematics, 1996, 5(1): 173-187.
[26] N.K. Suryadevara, S.C. Mukhopadhyay, R. Wang, R.K. Rayudu, Forecasting the behavior of an
elderly using wireless sensors data in a smart home, Engineering Applications of Artificial
Intelligence, Volume 26, Issue 10, November 2013, Pages 2641-2652, ISSN 0952-1976,
http://dx.doi.org/10.1016/j.engappai.2013.08.004.
[27] Jones B. A greedy algorithm for a generalization of the reconstruction problem, International Journal
of General System, 1985, 11(1): 63-68.
[28] Leongwai Yie, Joel Than Chia Ming, Features of Sleep Apnea Recognition and Analysis,
International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 2, pp, 481 – 497, 2014.
[29] Wenqing Che and Wang Tao, A Scene Recognition Algorithm Based on Multi-instance Learning,
International Journal on Smart Sensing and Intelligent Systems, vol. 7, no. 2, pp,1470 – 1492, 2014.

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