Face Recognition Based on Gabor Wavelet and S-ISOMAP

In this paper, a method is proposed to recognize the face using Gabor feature and supervised isometric feature mapping(S-ISOMAP). Since the original feature vectors may include redundancy such as high-order correlation which cannot be removed by manifold learning algorithms, Gabor wavelet is introduced as a method to extract their corresponding Gabor magnitude features (GMFs) by convolving the normalized face image with multi-scale andmulti-orientation Gabor filters. Then, S-ISOMAP operates on GMFs to extract the discriminative submanifolds. Furthermore, the nearest distance classifier is used for classification. The proposed method is robust toillumination, expression by combing the Gabor transform and supervised manifold learning.Experiments with YaleB and PIE databases show that the approach is quite effective.