Performance Comparison Of Face Recognition Algorithms Based On Face Image Retrieval

Résumé: Biometric systems are complex systems of safety measures based on physical, biological and the humans behavioral. The automatic face recognition has become a highly active research area, mainly due to numbers published papers in recent years. In this paper, we present a comparative study for evaluation of face recognition system based on face restoration. Our study is performed in two consecutive steps, In the first step, we use two methods of image restoration called Centralized sparse representation (CSR) and adaptive sparse domain selection with adaptive regularization (ASDS-AR) while in the second step the set of methods that have been used are principal component analysis (PCA), linear discriminant analysis (LDA) kernel principal component analysis (KPCA) and Kernel Fisher Analysis (KFA) for face recognition and we associated the Gabor Wavelets and Phase Congruency in order to achieve the evaluation of our proposed model. In addition, the comparative analysis on the ORL database is also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in ”real-life” operating conditions. Our experimental results suggest that Gabor linear discriminant analysis (GLDA) ensures the most consistent verification rates across the tested ORL databases for both methods CSR and ASDS-AR

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