Single-Sample Face Recognition via Fusion Variant Dictionary

摘要：

　　This paper presents a novel method called sparse representation based classification via fusion variant dictionary (FSRC) for single-sample face recognition.There are two points to be highlighted in our method: (1) A specific preprocessing step is introduced to help the gray level of the testing sample distributed uniformly.(2) A fusion variant dictionary is proposed including two parts: the first part is an intra-class variant term,which can help represent the moderate illuminations,expressions and disguises; the second part is a noise term,which can help remove the common noise (caused by pixel noise,severe illumination or our preprocessing step) in testing samples.Extensive experiments on public face databases demonstrate advantages of the proposed method over the state-of-the-art methods,especially in dealing with image corruption and severe illumination.

关键词： Single-sample face recognition sparse representation noise term

作者: Ying Tai Jian Yang Jianjun Qian Yu Chen

作者单位: Nanjing University of Science and Technology Nanjing 210094,P.R. China

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 341-350

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）