Integration of Global and Local Feature for Face Recognition
基于全局和局部特征集成的人脸识别

This paper proposes to combine the global and local facial features in both serial and parallel manner. Firstly, global features are used for coarse classification. Then, global and local features are integrated for fine classification. In the proposed method, global and local features are extracted by Discrete Fourier Transform (DFT) and Gabor Wavelets Transform (GWT) respectively. Experiments on two large scale face databases (FERET and FRGC v2.0) validate that the proposed method can not only greatly increase the system accuracy but also improve the system speed.