Multiple classifier implementation of a divide-and-conquer approach using appearance-based statistical methods for face recognition
This paper presents a multiple classifier system for the face recognition problem-based on a novel divide-and-conquer approach using appearance-based statistical methods, namely principal component analysis (PCA), linear discriminant analysis (LDA) and independent component analysis (ICA). A facial image is divided into a number of horizontal segments and the associated local features are extracted using a particular statistical method. Using a simple distance measure and an appropriate classifier combination method, facial images are successfully classified. The standard FERET database and the FERET evaluation methodology are used in all experimental evaluations. Computational and storage space efficiencies and experimental recognition performance of the proposed approach indicate that significant achievements are obtained compared to individual classifiers.
appearance-based statistical methods multiple classifier systems classifier combination local feature-based face recognition