Face Recognition Based on Gabor Features and Two-Dimensional PCA

• Main Authors: Yi-Chun LEE, 李易俊
• Other Authors: Chin-Hsing Chen
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/48225445944049222898

碩士 === 國立成功大學 === 電腦與通信工程研究所 === 93 === 　Pattern recognition and computer vision have witnessed the growing interests in face recognition problems. Current systems have advanced to be fairly accurate in recognition. But they still unable overcome the large variations, such as viewing directions or poses, facial expression, illumination conditions, aging, and disguises (facial hair, glasses or cosmetics). 　This thesis presents a new face recognition method based on Two-Dimensional Principal Component Analysis (2DPCA) and Gabor filters. In the method, an original image is convolved with 40 Gabor filters corresponding to various orientations and scales to give its Gabor representation. Then, the Gabor representation is analyzed by the 2DPCA in which the eigenvectors are computed using the Gabor image covariance matrix without matrix-to vector conversion. The proposed 2DPCA+GF method combines the advantages from 2DPCA and Gabor filters. A different version of the 2DPCA+GF, called 2DPCA+MGF, is also studied. In the 2DPCA+MGF, some of Gabor images are replaced by the original spatial-domain image to give a mixture representation. 　Experiments based on the ORL database were then performed to compare the recognition rate between the PCA, the 2DPCA, the 2DPCA+GF and the 2DPCA+MGF methods using the 1-norm and 2-norm minimum distance classifiers. The former two methods (PCA and 2DPCA) were studied by others before. The study on the latter two methods (2DPCA+GF and 2DPCA+MGF) is our new contribution. We find that the recognition rate using 1-norm distance measure is better than the 2-norm measure in the 2DPCA+MGF method. It achieves 98.5% recognition rate by using 25 principal components of 2DPCA using the 1-norm distance classifier. Under the same condition, the 2DPCA+GF achieves 93% recognition rate, the 2DPCA achieves 90.5% recognition rate, the PCA achieves 76.5% recognition rate. This study further confirm that the Gabor representation carries more discriminating information than its counterpart, the spatial-domain representation and the 2DPCA has advantage over the PCA both in recognition rate and implementation complexity.