关键词: 集体表示, 人脸识别, 稀疏优化

Abstract: Recently, representation-based classifications, such as Sparse Representation Classification （SRC）, Collaborative 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.

Key words: joint-collective representation, face recognition, sparse optimization