Face Recognition Based on Maximum Sparse Coefficients of Object Region
作者单位：School of Electronic Information Engineering Nantong University Centre for Pattern Recognition and Machine IntelligenceConcordia University
会议名称：《2015年中国智能自动化学术会议》
会议日期：2015年
学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程]
基 金：supported by the National Natural Science Foundation of China(NO.61171077) University Science Research Project of Jiangsu Province(NO.12KJB510025) Nantong University Undergraduate Training Program for Innovation(NO.2013067) the Natural Sciences and Engineering Research Council of Canada
关 键 词：Face recognition Maximum sparse coefficient Occlusion
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 *** efficacy of the proposed approach is verified on publicly available ***, our method can achieve much better performance when the training samples are limited.