计算机工程与应用2017，Vol.53Issue(12)：16-20,98,6.DOI:10.3778/j.issn.1002-8331.1702-0140
集体表示分类方法及在人脸识别中的应用
Joint-collective representation classification method and application in face recogni-tion
摘要
Abstract
Recently, representation-based classifications, such as Sparse Representation Classification(SRC), Collabora-tive Representation Classification(CRC), etc. have attracted extensive attention. These methods use a single picture to recognize the testing subject but ignore the relationship among a collection of pictures which lead to inadequate details. To take advantage of the correlation of multiple images, this paper presents a collective representation classification in face recognition. The new method uses a matrix to represent all the testing images then the most compact representation error is evaluated for classification. Multi-representation helps to take account of the similarity and difference hidden in the testing image-set. Especially when the image set includes more side-face images than frontal ones, Joint-collective Representation Classification(JRC)outperforms the state-of-the-art method which is also validated by the practical experiments in two public databases.关键词
集体表示/人脸识别/稀疏优化Key words
joint-collective representation/face recognition/sparse optimization分类
信息技术与安全科学引用本文复制引用
马腾飞,王丽平..集体表示分类方法及在人脸识别中的应用[J].计算机工程与应用,2017,53(12):16-20,98,6.基金项目
国家自然科学基金(No.11471159,No.61661136001). （No.11471159,No.61661136001）
