Weighted Adaptive Face Recognition Based on Class Matrix and Feature Fusion
基于类矩阵和特征融合的加权自适应人脸识别

A new weighted adaptive algorithm of face recognition based on class matrix and feature fusion was proposed. Firstly, global features and local features of six key parts of faces were extracted respectively. Dynamic method of how to choose the weights of local features was given. Different weights could be gained for different training sets according to this method. So, the adaptive ability of algorithm was enhanced. Then, global and local features were fused with weights to get the eigen-matrix of samples. Secondly, a new weighted principal component analysis (PCA) method was designed to lower dimension for sample matrixes. Thirdly, the concept of class matrix was proposed, and formula of how to obtain the class matrix was given and proved. According to class matrix, a new projected rule was given. Finally, class matrix and tested samples were projected respectively through the proposed rules. Then, the final class that tested faces belonged to was declared according to the Euclidean distance. Experiments show that the proposed algorithm can deal with small sample problems in LDA effectively, and the results also indicate that it has good performance on speed and recognition rate.