Concerning the disadvantage of traditional face recognition algorithm, such as high dimension of extracted feature, a great deal of computation, a fast face recognition algorithm was proposed. The algorithm integrated the half face recognition scheme, Gabor filter, Gabor features selecting method based on mutual information, and the nearest neighbor method for frontal face recognition. The face images in training set and testing set were divided into the left half and the right half, one half of the face images was chosen by entropy maximum. The features of the face images were extracted by Gabor filter. Then the rank of discriminating capabilities of features can be estimated by evaluating the classification error on intra-set and extra-set based on weak classifier built by single feature．The Gabor features with small errors were selected．And at the same time, the mutual information between the selected features was examined．The nearest neighbor method was used to recognize the frontal face. The experimental results show that the proposed method has higher accuracy than the traditional half face recognition algorithm, and is of lower computational complexity than the traditional Gabor filter algorithm.

n the paper, theorys of Kernel Fisher Discriminant Analysis (KFDA) were researched and analysed. After applying KFDA in feature extracting, the performance of KFDA and that of Linear Fisher Discriminant Analysis (FDA) feature extracting algorithms were compared. Finally, a fast and simple multiclass classification algorithm of KFDA-based was proposed. The algorithm can classify multiclass fast and simply. First of all, multiclass samples were mapped into a high dimension kernel space. In the space, the same class samples were assembled together, the different class samples were perfectly separated. So the multiclass samples can be separate easily. Comparing with One-to-One algorithm and One-to-All algorithm, the experiment results indicate that our algorithm is certainly faster and simpler in classification than other two algorithms.