Discriminative Dimensionality Reduction Optimized by Dictionary Learning for Robust Face Recognition
Tang Xin-y · Jiguang zazhi · 2014
The performance of face recognition system is seriously impacted by illumination, expression, pose and occlusion variations, for which the algorithm of discriminative dimensionality reduction optimized by dictionary learning is proposed. Firstly, typical feature extraction algorithm PCA is used to initialize dimensionality reduction projection matrix. Then, dictionary and coefficient is computed and the dictionary can match with each other by jointing dimension reduction and dictionary learning. Finally, dictionary and projection matrix is outputted by using iterative algorithm, and classifier regularized by l2-norm is used to finish face recognition. The effectiveness and reliability of proposed algorithm has been verified by experiments on extended YaleB, AR and a wild face databases. Experimental results show that proposed algorithm has higher recognition accuracy than several other linear represent algorithms when dealing with robust face recognition.