Video Face Recognition Method Based on QPSO and Manifold Learning

The highly complex video scene and the inconsistent video acquisition equipment have made the unconstrained videos full of occlusion and face rotation, thereby, resulting in both low accuracy and unstable performance of video face recognition. To solve the problem, we propose a novel method by integrating the quantum behaved particle swarm optimization (QPSO) and the Riemannian manifold learning. It outperforms the existing state-of-art methods owing to the followed contributions: 1) the algorithm treats each face video as an image set, so that the texture features can be extracted from the aligned frame image; 2) the internal representation of video face is obtained by the QPSO Riemannian manifold, enabling the similarity measurement using the distance between convex hulls; 3) the classification is conducted using the common-practiced SVM classifier, to some extent, guaranteeing the good prediction performance. The experiments on both the YouTube Face database and the Honda/UCSD database have shown that the proposed algorithm is not only of higher accuracy, but also more robust to the illumination and expression changes, as compared to the other methods. Copyright © 2020 Acta Automatica Sinica. All rights reserved.