To address the reduced face recognition accuracy in uncontrolled conditions such as the change of illumination,countenance or posture,a face recognition algorithm was proposed based on discriminative dictionary learning and regularized robust coding.Firstly,a face image is filtered by the Gabor filter to obtain the Gabor amplitude images,and the uniform local binary histogram is extracted.Then the Fisher criterion is used to gain a new discriminative dictionary,finally the regularized sparse representation is employed to test and classify the image.The experimental results based on AR face database show that the proposed algorithm has the highest face recognition rate in the existing uncontrolled environments,compared with algorithms such as Sparse Representation based Classifier,Fisher Discrimination Dictionary Learning,and Robust Sparse Coding for face recognition.