Extended cooperative representation algorithm based on undersample mixed internal variable base dictionary

摘要

The cooperative representation algorithm has the characteristics of rapid classification of face images, but in the case of single or undersample, the complex change of face recognition rate is not ideal enough to meet the engineering requirements. For this problem, a cooperative representation of face recognition algorithm with a mixed internal variable-base sparse dictionary is proposed. Firstly, with the help of the change process of different faces collected in the same environment, the common features of the change of the face are extracted and the invariant basis is generated, the generality of the invariant basis generated by the common features of two or more different changes of the face is improved, and the sparse dictionary of the change between the training sample and the test sample is established. With the help of the dictionary, the training samples can construct the feature faces of the test samples approximately, so as to expand the training sample set, the characteristic face of the test sample is constructed. Using the AR, ORL, Yale and Yale B library for identification experiments, the results show that this algorithm can effectively improve the recognition rate of the cooperative representation algorithm, and increase the recognition rate by 7.33 % to 33.17 % in the case of undersamples, and 6.78 % to 24.47 % in the case of single samples.