A MANOVA of LBP Features for Face Recognition

Face recognition is one of the most broadly researched subjects in pattern recognition. Feature extraction is a key step in face recognition. As an effective texture description operator, Local Binary Pattern (LBP) feature is firstly introduced by Ahonen et al into face recognition. Because of the advantages of simplicity and efficiency, LBP feature is widely applied and later on becomes one of the bench mark feature for face recognition. The basic idea of LBP feature is to calculate the binary relation between the central pixel and its local neighborhood. The images are described with a multi-regional histogram sequence of the LBP coded pixels. Since most of the LBP pattern of the images are uniform patterns, Ojala et al, 2002 proposed Uniform Local Binary Pattern (ULBP). Through discarding the direction information of the LBP feature, they proposed the Rotation Invariant Uniform Local Binary Pattern (RIU-LBP) feature. The Uniform LBP feature partly reduces the dimension and retains most of the image information. RIU-LBP greatly reduces the dimension of the feature, but its performance in face recognition decreases drastically. This chapter mainly discusses the major factors of the ULBP and RIU-LBP features and introduces an improved RIU-LBP feature based on the factor analysis. Many previous works also endeavored to modify the LBP features. Zhang and Shan et al, 2006 proposed Histogram Sequence of Local Gabor Binary Pattern (HSLGBP), whose basic idea was to perform LBP coding to the image in multi-resolution and multi-scale of the images, thereby enhancing the robustness to the variation of expression and illumination; Jin et al, 2004 handled the center pixel value as the last bin of the binary sequence, the formation of the new LBP operator could effectively describe the local shape of face and its texture information; Zhang and Liao et al, 2007a, 2007b proposed multi-block LBP algorithm (MB-LBP), the mean of pixels in the center block and the mean of pixels in the neighborhood block were compared; Zhao & Gao, 2008 proposed an algorithm for multi-directional binary mode (MBP) to perform LBP coding from four different directions; Yan et al, 2007 improved the robustness of the algorithm by fusing the mult-radius LBP feature; He et al, 2005 believed that every sub-block contained different information, and proposed an enhanced LBP feature. The original image was decomposed into four spectral images to calculate the Uniform LBP codes, and then the waterfall model was used to combine them as the final feature. In order to effectively extract the global and local features of face images, Wang Wei et al 2009 proposed LBP pyramid algorithm. Through multi-scale analysis, the algorithm