电信科学 ›› 2017, Vol. 33 ›› Issue (3): 52-58.doi: 10.11959/j.issn.1000-0801.2017057

• 研究与开发 • 上一篇    下一篇

采用双字典协作稀疏表示的光照及表情顽健人脸识别

龚飞,金炜,朱珂晴,符冉迪,曹燕   

  1. 宁波大学信息科学与工程学院，浙江 宁波315211
  • 修回日期:2017-02-23 出版日期:2017-03-01 发布日期:2017-04-05
  • 作者简介:龚飞（1989-），男，宁波大学信息科学与工程学院硕士生，主要研究方向为模式识别、压缩感知和图像处理。|金炜（1969-），男，博士，宁波大学信息科学与工程学院副教授、硕士生导师，主要从事压缩感知、模式识别和数字图像处理等研究工作。|朱珂晴（1989-），女，宁波大学信息科学与工程学院硕士生，主要研究方向为模式识别和图像处理。|符冉迪（1971-），男，宁波大学信息科学与工程学院副教授、硕士生导师，主要从事数字图像处理、模式识别等研究工作。|曹燕（1993-），女，宁波大学信息科学与工程学硕士生，主要研究方向为数字图像处理、模式识别。
  • 基金资助:
    国家自然科学基金资助项目(61471212);浙江省自然科学基金资助项目(LY16F010001);宁波市自然科学基金资助项目(2016A610091)

Illumination and expression robust face recognition using collaboration of double-dictionary's sparse representation-based classification

Fei GONG,Wei JIN,Keqing ZHU,Randi FU,Yan CAO   

  1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China
  • Revised:2017-02-23 Online:2017-03-01 Published:2017-04-05
  • Supported by:
    The National Natural Science Foundation of China(61471212);The Natural Science Foundation of Zhejiang Province of China(LY16F010001);The Natural Science Foundation of Ningbo of China(2016A610091)

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摘要：

提出一种采用小波变换（WT）及双字典协作稀疏表示分类（CSRC）的人脸识别方法——WT-CSRC。WT-CSRC首先利用PCA（主成分分析）将小波分解后的人脸高频细节子图融合成高频细节图像；然后用PCA分别对人脸低频图像和高频细节图像进行特征提取，构造低频和高频特征空间，并用训练样本在两种特征空间上的投影集构造低频字典和高频字典；最后将测试样本在两种字典上进行稀疏表示，并引入互相关系数以增强人脸识别的可靠性，实现了人脸的协作分类。实验结果表明，提出的方法提高了人脸识别率，对光照变化及表情变化具有较强的顽健性，并且具有较高的时间效率。

关键词: 人脸识别, 双字典, 协作稀疏表示, 互相关系数

Abstract:

A face recognition method named WT-CSRC was proposed by using wavelet transform (WT) and a collaboration of double-dictionary's sparse representation-based classification (CSRC). Firstly, the proposed method used principal component analysis (PCA) to achieve the fusion of three high-frequency detail sub-images which were generated by WT, and a integrated high-frequency detail image could be obtained; then, features extracted from the low-frequency images and high-frequency detail images by PCA were used to construct the low-frequency feature space and high-frequency detail space; and low-frequency dictionary and high-frequency dictionary could be constructed by samples' projection on two kinds of feature space. Finally, face images could be classified by a collaborative classification via sparse representation in two dictionaries, and the reliability of the recognition could be enhanced by using the cross correlation coefficient. Experimental results show that, the proposed method has high recognition rate with strong illumination and expression robustness with acceptable time efficiency.

Key words: face recognition, double-dictionary, collaborative sparse representation, cross correlation coefficient

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