Robust Facial Attribute-Specific Subspace-Based Principal Component Analysis for Face Recognition

Facial attribute-specific subspace-based PCA (FASS-based PCA) considers the information of class labels, and the discriminant power can be improved. However, it doesn't consider the outliers which are .common in realistic training sets. To address this problem, we propose robust facial attribute-specific subspace-based PCA (robust FASS-based PCA) algorithm in this paper, which gives a new weighted method for the evaluation of the total squared error of each class. The detailed algorithm for robust FASS-based PCA is also given. The results of experiments conducted on Yale databases show the effectiveness of the new feature extraction algorithm.