Discriminant Analysis of the two dimensional Gabor Features for face recognition

In this paper, a new technique called Two Dimensional Gabor Fisher Discriminant (2DGFD) is derived and implemented for image representation and recognition. In our approach, the Gabor wavelets are used to extract facial features. The Principal Component Analysis (PCA) is applied directly on the Gabor transformed matrices to remove redundant information from the image rows and a new direct two dimensional Fisher Linear Discriminant (direct 2DFLD) method is derived in order to further remove redundant information and form a discriminant representation more suitable for face recognition. The conventional Gabor based methods transform the Gabor images into a high-dimensional feature vector. However, these methods lead to high computational complexity and memory requirements. Furthermore, it is difficult to analyse such high dimensional data accurately. The novel 2DGFD method was tested on face recognition using the ORL, Yale and Extended databases, where the images vary in illumination, expression, pose, and scale. In particular, the 2DGFD method achieves 98.0% face recognition accuracy when using 20×3 feature matrices for each Gabor output on the ORL database and 97.6% recognition accuracy compared to 91.8% and 91.6% for the 2DPCA and 2DFLD method on the Extended Yale database. The results show that the proposed 2DGFD method is computational more efficient than the Gabor Fisher Classifier method (GFC) by approximately 8 times on the ORL, 135 times on the Yale and 1.2801×10 8 times on the Extended Yale B datasets.