A novel fisher discriminant for biometrics recognition: 2DPCA plus 2DFLD

In this paper, a method of two dimensional Fisher principal component analysis (2D-FPCA) in the two dimensional principal component analysis (2DPCA) transformed space is analyzed and its nature is revealed, i.e., 2D-FPCA is equivalent to 2DPCA plus two dimensional Fisher linear discriminant analysis (2DFLD). Based on this result, a more transparent 2D FPCA algorithm is developed. That is, 2DPCA is performed first and then 2DFLD is used for the second feature extraction in the 2DPCA transformed space. Since 2D FPCA is based on the 2D image matrices, the vectorization of the image is not required. Thus, 2D FPCA optimizes the evaluation of the image matrices, the between and within matrices, by transforming them into a smaller 2DPCA space. In the Linear Discriminant Analysis (LDA) based face recognition techniques, image representation and recognition is statistically dependent on the evaluation of the between and within matrices. This leads to the following benefits; the proposed 2D-FPCA yields greater recognition accuracy while reduces the overall computational complexity. Finally, the effectiveness of the proposed algorithm is verified using the ORL database as a benchmark. The new algorithm achieves a recognition rate of 95.50% compared to the recognition rate of 90.00% for the Fisherface method.