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| Face Recognition Based on Two-Dimensional Gabor Wavelets |
| Cao Lin①②; Wang Dong-feng①; Liu Xiao-jun①;Zou Mou-yan ①② |
| ①Institute of Electronics, Chinese Academy of Sciences, Beijing 100080, China; ② Graduate School of the Chinese Academy of Sciences, Beijing 100039, China; ③Dept. of Information and Telecommunication , Beijing Information Technology Inst., Beijing 100101, China |
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Abstract 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.
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Received: 09 August 2004
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2006, Vol. 28 
