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山东大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (3): 74-78.doi: 10.6040/j.issn.1672-3961.2.2015.075

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基于核贝叶斯压缩感知的人脸识别

周凯1,2,元昌安1,2*,覃晓1,郑彦2,冯文铎2   

  1. 1. 广西师范学院计算机与信息工程学院, 广西 南宁 530001;2. 广西大学计算机与电子信息学院, 广西 南宁 530004
  • 收稿日期:2015-05-20 出版日期:2016-06-30 发布日期:2015-05-20
  • 通讯作者: 元昌安(1964— ),男,安徽肥东人,教授,研究生导师,主要研究方向为机器学习与数据挖掘. E-mail:yca@gxtc.edu.cn E-mail:zhoukai456@yahoo.com
  • 作者简介:周凯(1991— ),男,湖南湘乡人,硕士研究生,主要研究方向为模式识别与图像处理. E-mail:zhoukai456@yahoo.com
  • 基金资助:
    国家自然科学基金资助项目(61363037)

Face recognition based on kernel Bayesian compressive sensing

ZHOU Kai1,2, YUAN Changan1,2*, QIN Xiao1, ZHENG Yan2, FENG Wenduo2   

  1. 1. College of Computer and Information Engineering, Guangxi Teachers Education University, Nanning 530001, Guangxi, China;
    2. School of Computer, Electronics and Information, Guangxi University, Nanning 530004, Guangxi, China
  • Received:2015-05-20 Online:2016-06-30 Published:2015-05-20

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摘要： 为加快人脸识别速度和提高人脸识别率,将贝叶斯压缩感知算法进行核扩展并运用到人脸识别,改进局部特征统计方法,结合空间金字塔模型,用于人脸图像的特征提取。首先用局部特征统计提取图像特征,在此基础上再进行第二层局部统计,然后根据空间金字塔模型分层提取不同空间尺度的特征,最后运用核贝叶斯压缩感知算法分类。在AR和FERET人脸数据库上的试验结果表明,本研究算法相对于传统方法具有更好的性能。

关键词: 核函数, 局部特征统计, 贝叶斯压缩感知, 空间金字塔, 人脸识别

Abstract: In order to improve the speed and rate of face recognition, Bayesian compressive sensing algorithm was applied and its kernel extension to face recognition was proposed. Combined with the spatial pyramid model, statistical local feature was improved to extract the features of face images. Firstly, the statistical local feature was used as a feature extractor to obtain facial features and a second layer of local statistics was processed based on the former layer. Then the spatial pyramid was used to obtain features in different spatial scales in order to accomplish the final step of face recognition, the features were classified through kernel Bayesian compressive sensing. The experimental results on the basis of the AR and FERET databases demonstrated that this algorithm had better performance than other traditional ones.

Key words: face recognition, kernel function, statistical local feature, spatial pyramid model, Bayesian compressive sensing

中图分类号: 

  • TP391
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