自动化学报2018，Vol.44Issue(12)：2179-2187,9.DOI:10.16383/j.aas.2018.c170090
基于改进的Fisher准则的多示例学习视频人脸识别算法
Video Face Recognition Based on Modified Fisher Criteria and Multi-instance Learning
摘要
Abstract
Due to the pose variation of target in video, it is difficult to accurately locate the face key frame and have a high recognition rate of the video face recognition based on key frame identification. To solve these problems, a video face recognition algorithm based on multi-instance learning is proposed in this paper. The algorithm takes each face video as a bag, and each normalized face frame as an instance in the bag. The feature of each instance is represented by cascading histograms of block TPLBP codes, and the weight of the instance feature is obtained by the improved Fisher criteria.The classifier is obtained in the feature space of training set by using a multiple instance learning algorithm, and then classification and prediction of test bag are realized. Experiments on the Honda/UCSD and YouTube Face databases show that the algorithm can achieve a higher recognition accuracy, and at the same time, the method is robust to illumination variation and expression variation.关键词
视频人脸识别/局部二值模式/多示例学习/Fisher准则Key words
Video-based face recognition/local binary patterns (LBP) /multi-instance learning/Fisher criteria引用本文复制引用
王玉,申铉京,陈海鹏..基于改进的Fisher准则的多示例学习视频人脸识别算法[J].自动化学报,2018,44(12):2179-2187,9.基金项目
国家青年科学基金(61305046,61602203) （61305046,61602203）
吉林省优秀青年人才基金(20180520020JH)资助 （20180520020JH）
