一种基于QPSO优化的流形学习的视频人脸识别算法

视频场景复杂多变,视频采集设备不一致等原因,导致无约束视频中充斥着大量的遮挡和人脸旋转,视频人脸识别方法的准确率不高且性能不稳定.为解决上述问题,本文提出了一种基于QPSO优化的流形学习的视频人脸识别算法.该算法将视频人脸识别视为图像集相似度度量问题,首先帧图像对齐后提取纹理特征并进行融合,再利用带有QPSO优化的黎曼流形大幅度简约维度以获得视频人脸的内在表示,相似度则由凸包距离表示,最后利用SVM分类器获得分类结果.通过在Youtube Face数据库和Honda/UCSD数据库上与当前主流算法进行的对比实验,验证了本文算法的有效性,所提算法识别精度较高,误差较低,并且对光照和表情变化具有较强的鲁棒性.

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.