Discriminative Principal Component Analysis: A REVERSE THINKING
This work addresses face recognition challenges by combining PCA and LDA, offering an incremental improvement for domain-specific applications.
The authors tackled the problem of enhancing separability in Principal Component Analysis (PCA) by integrating Linear Discriminant Analysis (LDA), proposing Discriminative PCA to improve face recognition accuracy. Results showed superior recognition rates and comparable running times compared to existing methods on four facial databases.
In this paper, we propose a novel approach named by Discriminative Principal Component Analysis which is abbreviated as Discriminative PCA in order to enhance separability of PCA by Linear Discriminant Analysis (LDA). The proposed method performs feature extraction by determining a linear projection that captures the most scattered discriminative information. The most innovation of Discriminative PCA is performing PCA on discriminative matrix rather than original sample matrix. For calculating the required discriminative matrix under low complexity, we exploit LDA on a converted matrix to obtain within-class matrix and between-class matrix thereof. During the computation process, we utilise direct linear discriminant analysis (DLDA) to solve the encountered SSS problem. For evaluating the performances of Discriminative PCA in face recognition, we analytically compare it with DLAD and PCA on four well known facial databases, they are PIE, FERET, YALE and ORL respectively. Results in accuracy and running time obtained by nearest neighbour classifier are compared when different number of training images per person used. Not only the superiority and outstanding performance of Discriminative PCA showed in recognition rate, but also the comparable results of running time.