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At present, the pose change of the face test sample is the main reason that affects the accuracy of face recognition, and the design of cross-pose recognition algorithm is a technical problem to be solved. In this paper, a cross-pose face recognition algorithm which integrates the regression iterative method and the interactive subspace method was proposed, and through the regression iteration, the target function converges rapidly and important characteristics of the sample were extracted. Then, the posture of cross-pose face image was estimated, and finally, the interactive subspace method was applied to judge the similarity of the human face. The experimental results of FERET face database (± 45°and ± 90° posture) and MIT-CBCL face database (N-fold cross-validation) showed that the proposed RIM-ISM algorithm had a higher recognition rate and robustness, and it could effectively solve the difficulty of cross-pose face recognition.

Cross-pose face recognition by integrating regression iteration and interactive subspace