Face Recognition Based on Bidirectional Gradient Center-Symmetric Local Binary Patterns

To overcome the limitations of traditional face recognition methods for single sample, a novel method of face recognition based on bidirectional gradient center-symmetric local binary pattern(BGCSBP) is proposed. Firstly horizontal gradient and vertical gradient of face image are calculated, and center-symmetric local binary pattern(CS-LBP) is proposed to encode the gradient. Secondly the proposed BGCSBP is the combination of the CS-LBP of horizontal gradient and vertical gradient. BGCSBP feature maps are divided into several blocks and the concatenated histogram features calculated over all blocks are used for the feature descriptor of face recognition, and the recognition is performed by using the histogram cross. This experimental results on CAS-PEAL, Extend Yale B and AR face databases show that the algorithm is simple and effective, and robust to variations of face illumination, face expression and partial occlusion conditions.