Local binary patterns and its variants for face recognition

Face recognition is one of the most important tasks in computer vision and Biometrics. Texture is an important spatial feature useful for identifying objects or regions of interest in an image. Texture based face recognition is widely used in many applications. LBP method is most successful for face recognition. It is based on characterizing the local image texture by local texture patterns. In this paper performance evaluation of Local Binary Pattern (LBP) and its modified models Multivariate Local Binary Pattern (MLBP), Center Symmetric Local Binary Pattern (CS-LBP) and Local Binary Pattern Variance (LBPV) are investigated. Facial features are extracted and compared using K nearest neighbour classification algorithm. G-statistics distance measure is used for classification. Experiments were conducted on JAFFE female, CMU-PIE and FRGC version 2 databases. The results shows that CS-LBP consistently performs much better than the remaining other models.