An improvement on face recognition rate using local tetra patterns with support vector machine under varying illumination conditions

Varying illumination condition make face recognition a challenging issue. In this paper, we propose a novel approach for solving this problem by performing three steps: (1) illumination normalization that normalizes the images using pre-processing chain of Gamma Correction, Difference of Gaussian and Contrast Equalization; (2) local tetra patterns (LTrPs) for texture based face representation; and (3) Support Vector Machine (SVM) for classification. The experimental results shows that face recognition rate of proposed system is improved from 97.9%, 97% and 99% to 99.34% as compared with LDP using Histogram Intersection, LBP using Chi-square, and LTP using Euclidean Distance respectively on Extended Yale-B database.

[1]  J. Koenderink.  Q… , 2014, Les noms officiels des communes de Wallonie, de Bruxelles-Capitale et de la communaute germanophone.

[2]  Wang Beizhan,et al.  Improved Generalized Co-occurrence Matrix for Image Retrieval using MPEG-7 Texture Edge , 2008, 2008 International Symposium on Information Science and Engineering.

[3]  Xu De,et al.  Region-based image retrieval using color coherence region vectors , 2004, Proceedings 7th International Conference on Signal Processing, 2004. Proceedings. ICSP '04. 2004..

[4]  Baochang Zhang,et al.  Local Derivative Pattern Versus Local Binary Pattern: Face Recognition With High-Order Local Pattern Descriptor , 2010, IEEE Transactions on Image Processing.

[5]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  阿部 重夫,et al.  Support vector machines for pattern classification , 2005 .

[7]  Subrahmanyam Murala,et al.  Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval , 2012, IEEE Transactions on Image Processing.

[8]  Mark A. Girolami,et al.  An empirical analysis of the probabilistic K-nearest neighbour classifier , 2007, Pattern Recognit. Lett..

[9]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[10]  Browne,et al.  Cross-Validation Methods. , 2000, Journal of mathematical psychology.

[11]  Fatma Zohra Chelali,et al.  Linear discriminant analysis for face recognition , 2009, 2009 International Conference on Multimedia Computing and Systems.

[12]  I. Thusnavis Bella Mary,et al.  Content based image retrieval using textural features based on pyramid-structure wavelet transform , 2011, 2011 3rd International Conference on Electronics Computer Technology.

[13]  Zia-ur Rahman,et al.  A multiscale retinex for bridging the gap between color images and the human observation of scenes , 1997, IEEE Trans. Image Process..

[14]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[15]  S. Anila,et al.  Preprocessing Technique for Face Recognition Applications under Varying Illumination Conditions By , 2012 .

[16]  Subrahmanyam Murala,et al.  Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval , 2014, IEEE Journal of Biomedical and Health Informatics.

[17]  Ralph Gross,et al.  An Image Preprocessing Algorithm for Illumination Invariant Face Recognition , 2003, AVBPA.

[18]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Ghazali Sulong,et al.  Illumination normalization for edge-based face recognition using the fusion of RGB normalization and gamma correction , 2013, 2013 IEEE International Conference on Signal and Image Processing Applications.

[20]  Shigeo Abe,et al.  Variants of Support Vector Machines , 2010 .

[21]  Wing W. Y. Ng,et al.  Content-based image retrieval using color moment and Gabor texture feature , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[22]  Dorin Comaniciu,et al.  Total variation models for variable lighting face recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Shu Liao,et al.  Dominant Local Binary Patterns for Texture Classification , 2009, IEEE Transactions on Image Processing.