Face Recognition with Local Line Binary Pattern

In this paper, we introduce a novel face representation method for face recognition, called Local Line Binary Pattern (LLBP), which is motivated from Local Binary Pattern (LBP) due to it summarizes the local spacial structure of an image by thresholding the local window with binary weight and introduce the decimal number as a texture presentation. Moreover it consumes less computational cost. The basic idea of LLBP is to first obtain the Line binary code along with horizontal and vertical direction separately and its magnitude, which characterizes the change in image intensity such as edges and corners, is then computed. Our experimental result is evaluated on the public Yale face database B as well as its Extended version and FERET database by using Linear Discriminant Analysis (LDA) as classification. The comparative results have shown that the LLBP is more discriminative and insensitive to illumination variation and facial expression than other methods.

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

[2]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[3]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.

[4]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[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]  Qian Tao,et al.  Illumination Normalization Based on Simplified Local Binary Patterns for A Face Verification System , 2007, 2007 Biometrics Symposium.

[7]  Haitao Wang,et al.  Face recognition under varying lighting conditions using self quotient image , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[8]  Hanqing Lu,et al.  Face detection using improved LBP under Bayesian framework , 2004, Third International Conference on Image and Graphics (ICIG'04).

[9]  Paul A. Viola,et al.  Robust Real-time Object Detection , 2001 .

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

[11]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

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

[13]  Dorin Comaniciu,et al.  Illumination normalization for face recognition and uneven background correction using total variation based image models , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[15]  Haitao Wang,et al.  Generalized quotient image , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[16]  B. K. Julsing,et al.  Face Recognition with Local Binary Patterns , 2012 .

[17]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Shengcai Liao,et al.  Learning Multi-scale Block Local Binary Patterns for Face Recognition , 2007, ICB.

[19]  Guodong Guo,et al.  Face recognition by support vector machines , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[20]  Donald Goldfarb,et al.  Second-order cone programming , 2003, Math. Program..