Centre symmetric quadruple pattern-based illumination invariant measure

作     者：Hu Changhui Zhang Yang Lu Xiaobo Liu Pan 胡长晖;张扬;路小波;刘攀

作者机构：School of AutomationSoutheast UniversityNanjing 210096China Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of EducationSoutheast UniversityNanjing 210096China School of TransportationSoutheast UniversityNanjing 211189China 

出 版 物：《Journal of Southeast University(English Edition)》 (东南大学学报（英文版）)

年 卷 期：2020年第36卷第4期

页      面：407-413页

核心收录：

学科分类：1305[艺术学-设计学（可授艺术学、工学学位）] 13[艺术学] 081104[工学-模式识别与智能系统] 08[工学] 0804[工学-仪器科学与技术] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 

基　　金：The National Natural Science Foundation of China(No.61802203) the Natural Science Foundation of Jiangsu Province(No.BK20180761) China Postdoctoral Science Foundation(No.2019M651653) Postdoctoral Research Funding Program of Jiangsu Province(No.2019K124) 

主　　题：centre symmetric quadruple pattern illumination invariant measure severe illumination variations single sample face recognition 

摘      要：A centre symmetric quadruple pattern-based illumination invariant measure(CSQPIM)is proposed to tackle severe illumination variation face ***,the subtraction of the pixel pairs of the centre symmetric quadruple pattern(CSQP)is defined as the CSQPIM unit in the logarithm face local region,which may be positive or *** CSQPIM model is obtained by combining the positive and negative CSQPIM ***,the CSQPIM model can be used to generate several CSQPIM images by controlling the proportions of positive and negative CSQPIM *** single CSQPIM image with the saturation function can be used to develop the *** CSQPIM images employ the extended sparse representation classification(ESRC)as the classifier,which can create the CSQPIM image-based classification(CSQPIMC).Furthermore,the CSQPIM model is integrated with the pre-trained deep learning(PDL)model to construct the CSQPIM-PDL ***,the experimental results on the Extended Yale B,CMU PIE and Driver face databases indicate that the proposed methods are efficient for tackling severe illumination variations.