Multi-view face recognition based on factor analysis and sparse representation

Under uncontrolled environment,one of the greatest remaining research challenges in face recognition is to recognize faces across different poses and occlusion.The face recognition method via Sparse Representation(SRC)considers that the test image can be represented as a sparse linear combination of the training images,and further use the combination coefficients for face recognition.This method is robust to face occlusion and noise,but poor performance to face pose varying.The reason is that SRC requires exact alignment between each testing and training image,the variation of pose results in alignment error which is contrary to the prerequisite of the linear combination.In order to overcome of face occlusion and pose variations problem,this paper applies factor analysis to human faces to separate the face pose factors and obtain virtual frontal faces for SRC face recognition.Experimental results demonstrate that the presented algorithm possesses good robustness for the face variation of poses and occlusion.