Multi-modal face parts fusion based on Gabor feature for face recognition

A novel face recognition method, which is a fusion of multi-modal face parts based on Gabor feature (MMP-GF), is proposed in this paper. Firstly, the bare face image detached from the normalized image was convolved with a family of Gabor kernels, and then according to the face structure and the key-points locations, the calculated Gabor images were divided into five parts: Gabor face, Gabor eyebrow, Gabor eye, Gabor nose and Gabor mouth. After that multi-modal Gabor features were spatially partitioned into non-overlapping regions and the averages of regions were concatenated to be a low dimension feature vector, whose dimension was further reduced by principal component analysis (PCA). In the decision level fusion, match results respectively calculated based on the five parts were combined according to linear discriminant analysis (LDA) and a normalized matching algorithm was used to improve the performance. Experiments on FERET database show that the proposed MMP-GF method achieves good robustness to the expression and age variations.