View Independent Face Detection Based on Combination of Local and Global Kernels

Abstract

In this paper, local and global kernels are combined to use the detailed and rough similarities simultaneously. In recent years, many recognition methods based on local features were proposed. However, the combination of only local matching is not sufficient. Global viewpoint is also necessary to improve the generalization ability. Local feature matching measures the detailed similarity and global feature matching measures the rough similarity. Therefore, the error pattern is different in local and global features. If they are combined well, the generalization ability is improved. In the proposed method, local kernels and global kernel are combined by summation, and the combined kernel is used in SVM. The proposed method is applied to view independent face detection task. We confirm that the false positive is reduced by combining local and global kernels. The effectiveness of the proposed method is demonstrated by the comparison with only global and local kernel.