earticle

영어

How to extract the robust discrimination features is the key of face recognition (FR). Local binary pattern is one of the most widely used feature extracting method in FR for its comprehensive representation of the visual content of face image. However, the feature vector extracted by LBP is usually very high-dimensional and maybe contains information redundancy. To deal with the drawback of LBP, a novel nonlinear version of LBP is presented. The main idea is firstly all the feature vectors extracted by LBP are mapped into a feature space by a nonlinear mapping, and then the mapped features are expressed using the corresponding projection vectors. Lastly, FR is performed based on the projection vectors. Compared with LBP, the new method has two advantages. Firstly, it can capture the nonlinear information of the feature vector extracted by LBP. Secondly, it avoids the complex expression of the nonlinear mapping. The experimental results on two public standard visual face datasets demonstrate the proposed method is superior to LBP in recognition accuracy while its computational complexity is considerably reduced.