Face recognition with statistical Local Binary Patterns

In this work, we present a novel algorithm for face recognition named statistical Local Binary Patterns (sLBP). This is a further development of original Local Binary Pattern algorithm. Our method is applied for face recognition under visual light environment dealing with dramatically illumination varying on faces After a statistical analysis on the distribution probability of the gray-level difference values between neighbor pixels, a mapping function is proposed to encode a wide range of these values into three binary bits. Three extension LBP layers are then generated Finally the uniform pattern histograms of all these layers in every divided region are concatenated as an enhanced local feature vector of the face image. Experimental results on FERET face database show considerable effectiveness and robustness of our proposed method.

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

[2]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[3]  Wang Xiaotong,et al.  Neighborhood Limited Empirical Mode Decomposition and Application in Image Processing , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[4]  Hong Yang,et al.  A LBP-based Face Recognition Method with Hamming Distance Constraint , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[5]  Tieniu Tan,et al.  Combining Statistics of Geometrical and Correlative Features for 3D Face Recognition , 2006, BMVC.

[6]  Matti Pietikäinen,et al.  Face Recognition with Local Binary Patterns , 2004, ECCV.

[7]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[9]  Hyeonjoon Moon,et al.  The FERET evaluation methodology for face-recognition algorithms , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[11]  Di Huang,et al.  A robust infrared face recognition method based on adaboost gabor features , 2007, 2007 International Conference on Wavelet Analysis and Pattern Recognition.