Face Recognition Using Holistic Features and Within Class Scatter-Based PCA
I Gede Pasek Suta Wijaya, Keiichi Uchimura, Gou Koutaki · GSTF international journal on computing/GSTF unternational journal on computing · 2013
Abstract The Principle Component Analysis (PCA) and its variations are the most popular approach for features clustering, which is mostly implemented for face recognition. The optimum projection matrix of the PCA is typically obtained by eigenanalysis of global covariance matrix. However, the projection data using the PCA are lack of discriminatory power. This problem is caused by removing the null space of data scatter that contains much discriminant information. To solve this problem, we present alternative strategy to the PCA called alternative PCA, which obtains the optimum projection matrix from within class scatter instead of global covariance matrix. This algorithm not only provides better features clustering than that of common PCA (CPCA) but also can overcome the retraining problem of the CPCA. In this paper, this algorithm is applied for face recognition with the holistic features of face image, which has compact size and powerful energy compactness as dimensional reduction of the raw face image. From the experimental results, the proposed method provides better performance for both recognition rate and accuracy parameters than those of CPCA and its variations when the tests were carried out using data from several databases such as ITS-LAB., INDIA, ORL, and FERET.