Face Recognition Based on Gabor Features and Two-Dimensional PCA

This paper 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. 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. We find that the recognition rate using 1-norm distance measure is better in the 2DPCA+MGF method. It achieves 98.5% recognition rate by using 25 principal components of 2DPCA using the 1-norm distance classifier.