To overcome the problems of the illumination and pose variations in image recognition

the algorithm of Gaussian mixture sparse representation for image recognition based on dictionary learning and Gabor features is proposed.Based on the maximum likelihood estimation principle

a mixture Gaussian sparse coding model is proposed to express the discriminating items to the maximum likelihood function of residuals

so the problem of identification is converted to the optimal weighted norm approximation problem.This approach extracts the Gabor features of the images by the Gabor filter

and then uses the Gabor features to learn a new dictionary.As the Fisher criterion is added in the learning process as a constraint

a new dictionary with category labels can be obtained.Finally

the method of Gaussian mixture sparse representation is used for classification and identification.The experimental results in three public databases demonstrate that the algorithm proposed is effective and robust.