Face recognition algorithm using collaborative sparse representation based on CNN features

Considering that the algorithm accuracy of the traditional sparse representation models is not high under the influence of multiple complex environmental factors, this study focuses on the improvement of feature extraction and model construction. Firstly, the convolutional neural network(CNN) features of the face are extracted by the trained deep learning network. Next, the steady-state and dynamic classifiers for face recognition are constructed based on the CNN features and Haar features respectively, with two-stage sparse representation introduced in the process of constructing the steady-state classifier and the feature templates with high reliability are dynamically selected as alternative templates from the sparse representation template dictionary constructed using the CNN features. Finally, the results of face recognition are given based on the classification results of the steady-state classifier and the dynamic classifier together. Based on this, the feature weights of the steady-state classifier template are adjusted in real time and the dictionary set is dynamically updated to reduce the probability of irrelevant features entering the dictionary set. The average recognition accuracy of this method is 94.45% on the CMU PIE face database and 96.58% on the AR face database, which is significantly improved compared with that of the traditional face recognition methods.