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.