Abstract: In order to improve the recognition effect of multipose face, we designed a multipose face recognition algorithm, combining sparse coding and machine learning. Firstly, the multipose face was collected and preprocessed, and feature of face image based on sparse coding was extracted. Secondly, the feature was processed by principal component analysis to reduce the feature dimension of multipose face recognition and improve the efficiency of multipose face recognition. Finally, the support vector machine of machine learning algorithm was used to establish the classifier of multipose face recognition, and the performance of the algorithm was verified by the standard face database and multipose face database. The verification results show that the algorithm can effectively improve the accuracy of multipose face recognition, greatly reduce the average recognition time of the multipose face, and
achieve better recognition results than the contrast algorithm, thus the superiority of the algorithm is verified.

Key words: multipose face, recognition algorithm, sparse coding, principal component analysis, support vector machine