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Face recognition using principal component analysis of Gabor filter responses

This paper addresses a new face recognition method based on principal component analysis (PCA) and Gabor filter responses. Our method consists of two parts. One is Gabor filtering on predefined fiducial points that could represent robust facial features from the original face image. The other is transforming the facial features into eigenspace by PCA, which is able to classify individual facial representations. Thus, the trained face model has some eigenvalues that can be derived from an ensemble matrix of given Gabor responses. In order to identify the faces, test images are also projected into eigenspace from the image space and compared to the trained face images in the same eigenspace. The basic idea of containing the PCA and Gabor filter is to overcome the shortcomings of PCA. When raw images were used as a matrix of PCA, the eigenspace cannot reflect the correlation of facial features well, because original face images have deformation due to in-plane, in-depth rotation and brightness and contrast variation. So, we have overcome these problems using Gabor filter responses as input. A Gabor filter has the robust characteristics of illumination and rotation. In addition, we confirmed the improvement of discrimination ability when Gabor responses had transferred to the space constructed by the principal components. The experimental results show that the proposed method achieves the remarkable improvement of recognition rate of 19% and 11% compared to conventional PCA method in SAIT dataset and Olivetti dataset respectively. Our method has the advantage in gallery DB size than the recognition method only using Gabor filter responses.