Face Recognition Based on Two-Dimensional Gabor Wavelets
Lin Cao, Dongfeng Wang, Xiaojun Liu, Zou Mou‐Yan · 2006
A new approach based on two-dimensional Gabor wavelets transform for face recognition is presented. The Gabor wavelet representation of an image is the convolution of the image with a family of Gabor kernels. A set of vectors called nodes, over a dense grid of image points are formed, and each node is labeled with a set of complex Gabor wavelets coefficients. The magnitudes of the coefficients are used for recognition. Principal component analysis is a decorrelation technique and its primary goal is to project the high dimensional vectors into a lower dimensional space. Feature nodes, as observation vectors of HMM, is derived by using principal component analysis. A set of images representing different instances of the same person is used to train each HMM, and each individual in the database is represented by an optimal HMM face model. Experimental results show that the proposed algorithm has a high recognition rate with relatively low complexity.