Gabor-based Face Recognition With Illumination Variation Using Subspace-Linear Discriminant Analysis

Although Face recognition has been an active research topic in the past few decades due to its potential applications. Accurate face recognition is still a difficult task, especially in the case that illumination are unconstrained. This paper presents an efficient method for the recognition of faces with different illumination by using Gabor features, which are extracted by using log-Gabor filters of six orientations and four scales. By Using sliding window algorithm, these features are extracted at image block-regions . Extracted features are passed to the Principal Component Analysis (PCA) and then to Linear Discriminant Analysis (LDA). For development and testing we used facial images from the Yale-B  databases. The proposed method achieved 86 – 100 % rank 1 recognition rate .

[1]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[2]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Rabab Kreidieh Ward,et al.  Adaptive Region-Based Image Enhancement Method for Robust Face Recognition Under Variable Illumination Conditions , 2010, IEEE Transactions on Circuits and Systems for Video Technology.

[4]  Mauridhi Hery Purnomo,et al.  Appearance Global and Local Structure Fusion for Face Image Recognition , 2011 .

[5]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[7]  Bernard Gosselin,et al.  Character Segmentation-by-Recognition Using Log-Gabor Filters , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[8]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[9]  Shaohua Kevin Zhou,et al.  A comparison of subspace analysis for face recognition , 2003, 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698).

[10]  Chengjun Liu,et al.  Gabor-based kernel PCA with fractional power polynomial models for face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Bernt Schiele,et al.  Recognition without Correspondence using Multidimensional Receptive Field Histograms , 2004, International Journal of Computer Vision.

[12]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[13]  Rolf P. Würtz,et al.  Elastic Bunch Graph Matching , 2014, Scholarpedia.

[14]  Shaohua Kevin Zhou,et al.  A comparison of subspace analysis for face recognition , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..

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

[16]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[17]  Michael Brady,et al.  On the Choice of Band-Pass Quadrature Filters , 2004, Journal of Mathematical Imaging and Vision.

[18]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[19]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[20]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[21]  W. L. Woo,et al.  Discriminant analysis of the two-dimensional Gabor features for face recognition , 2008 .

[22]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

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

[24]  Hongzhou Zhang,et al.  Face Recognition using Gaborface-based 2DPCA and (2D)2PCA Classification with Ensemble and Multichannel Model , 2007, 2007 IEEE Symposium on Computational Intelligence in Security and Defense Applications.

[25]  LinLin Shen,et al.  Gabor wavelets and kernel direct discriminant analysis for face recognition , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..