视频人脸识别中判别性联合多流形分析

将基于视频的人脸识别转换为图像集识别问题,并提出两种流形来表示每个图像集:一种是类间流形,表示每个图像集的平均脸信息;另一种是类内流形,表示每个图像集的所有原始图像的信息.类间流形针对图像集之间的区别提取整体判别信息,作用是选出几个与待识别图像集较为相似的候选图像集.类内流形则考虑图像集内各原始图像之间的关系,负责从候选图像集中找出最为相似的一个.不同于现有的非线性流形方法中每幅图像对应流形中的一个点,采用分片技术学习两种流形的投影矩阵,每个分片对应流形中的一个点,所学到的特征更具有判别性,进而使流形边界更加清晰,同时解决了传统非线性流形方法中的角度偏差和不充分采样问题.还提出了与分片技术相匹配的流形之间的距离度量方法.最后在几个广为研究的数据集上进行了实验,结果表明:新方法的识别准确率高,尤其适用于不受控环境下的视频识别,而且不受视频段长短的影响.

Discriminative Joint Multi-Manifold Analysis for Video-Based Face Recognition

In this paper, video-based face recognition (VFR) is converted into image set recognition. Two types of manifolds are proposed to represent each gallery set: one is inter-class manifold which represents mean face information of this set, and the other is intra- class manifold corresponding to original images information of this set. The inter-class manifold abstracts discriminative information of the whole image set so as to select a few candidate gallery sets relevant to query set. The intra-class manifold chooses the most similar one from candidate sets by considering the relationships among all original images of each gallery set. Existing nonlinear manifolds methods project each image into low dimensional space as a point, thus suffer from cluster alignment and un-sufficient sampling. In order to avoid the above flaws and make the margin clearer between manifolds, projecting matrices in new method are gotten by means of partitioning image into un-overlapping patches so that features extracted this way can be more discriminative. In addition, a method of similarity measure between manifolds is proposed in accordance with the patching scheme. Finally, extensive experiments are conducted on several widely studied databases. The results demonstrate that new method achieves better performance than those state-of-the-art VFR methods, and it especially works well in un-controlled videos without being affected by the length of video.