Face recognition based on local binary pattern and improved Pairwise-constrained Multiple Metric Learning

During the image acquisition process of face recognition, the obtained face images are affected inevitably by varied illumination and position in different environment. Local Binary Pattern (LBP) operator is used to decrease illumination effectiveness. Improved Pairwise-constrained Multiple Metric Learning method (IPMML) is proposed as a classification metric in our prior work, which solves the misalignment problem in a better way compared with PMML. To solve the high computation complexity of IPMML, Linear Discriminant Analysis (LDA) is performed before IPMML. Thus, a face recognition method based on LBP and IPMML is proposed, which can overcome the illumination and misalignment problems. LBP is selected to extract texture features of face images firstly. Second, LDA is applied to reduce the dimension. Then the fisher features are divided into sub-blocks according to the dimension of features and every block is a column vector. Fourth, a classification metric -- IPMML is used to obtain the optimum Mahalanobis matrix. Fifth, the Mahalanobis matrix is used to compute the final discriminative distance. Finally, the Nearest Neighborhood Classifier (NNC) is applied to classify face images. The experimental results show that the proposed method can achieve high recognition rates and is robust to illumination and facial expression variation, especially for misaligned face images.