Face Recognition Based on Maximum Sparse Coefficients of Object Region

摘要：

　　Face recognition is an active topic in recognition systems,while face occlusion is one of the most challenging problems for recognition.Recently,robust sparse coding achieved the state-of-the-art performance,especially when dealing with occluded images.However,robust sparse coding is known that only guarantees the coefficient is global sparse when solving sparse coefficients.In this paper,we enable the elements in the object region to approximate global maximum by fitting the distribution of elements in the object region with successful recognition.The efficacy of the proposed approach is verified on publicly available databases.Furthermore,our method can achieve much better performance when the training samples are limited.

关键词： Face recognition Maximum sparse coefficient Occlusion

作者: Zineng Xu Hongjun Li Xiangyu Jin Ching Y. Suen

作者单位: School of Electronic Information Engineering,Nantong University,Nantong 226019,China School of Electronic Information Engineering,Nantong University,Nantong 226019,China;Centre for Patt Centre for Pattern Recognition and Machine Intelligence,Concordia University,Montreal,QC H3G 1M8,Can

会议类型: 国际会议

会议名称: The 2015 Chinese Intelligent Automation Conference(2015中国智能自动化会议)

会议地点: 福州

会议语种:英文

页码: 43-51

在线出版日期: 2015-05-08（万方平台首次上网日期，不代表论文的发表时间）