To recognize the face images containing errors of illumination

expression

pose

occlusion

or contaminated by noise

we propose a face image recognition method via Gabor low-rank recovery sparse representation-based classification.In this method

we firstly obtain the error images of the training images using the low-rank matrix recovery algorithm

and then calculate the Gabor feature vectors of the training images and the corresponding error images via the Gabor transform algorithm.With these Gabor feature vectors

we constitute a Gabor feature dictionary.Based on the Gabor feature dictionary

we calculate the sparse representation coefficients of Gabor feature vector of the given test image.For each class

we use the sparse representation coefficients associated with the class and the Gabor feature dictionary to reconstruct the Gabor feature vector of the given test image.And then we calculate the reconstruction error between the Gabor feature vector and its approximation associated with the class.Based on the reconstruction errors associated with different class

we can accurately classify the given test image.Experimental results on CMU PIE

Extend Yale B and AR databases show that the proposed face image recognition method has a higher recognition rate and greater noise immunity.