计算机应用 ›› 2014, Vol. 34 ›› Issue (1): 158-161.DOI: 10.11772/j.issn.1001-9081.2014.01.0158

• 虚拟现实与数字媒体 • 上一篇    下一篇

基于非下采样contourlet梯度方向直方图的人脸识别

奉俊鹏,杨恢先,蔡勇勇,翟云龙,李球球   

  1. 湘潭大学 材料与光电物理学院,湖南 湘潭 411105
  • 收稿日期:2013-07-22 修回日期:2013-09-15 出版日期:2014-01-01 发布日期:2014-02-14
  • 通讯作者: 奉俊鹏
  • 作者简介:奉俊鹏(1990-),男,湖南娄底人,硕士研究生,主要研究方向:图像处理、数字信号处理,模式识别;杨恢先(1963-),男,湖南益阳人,教授,主要研究方向:图像处理、人工智能;蔡勇勇(1988-),男,江苏南通人,硕士研究生,主要研究方向:模式识别、嵌入式系统;翟云龙(1988-),男,湖北荆门人,硕士研究生,主要研究方向:模式识别、嵌入式系统;李球球(1989-),女,湖南衡阳人,硕士研究生,主要研究方向:模式识别。
  • 基金资助:

    湖南省教育厅资助科研项目;湘潭大学资助科研项目

Face recognition based on histograms of nonsubsampled contourlet oriented gradient

FENG Junpeng,YANG Huixian,CAI Yongyong,ZHAi Yunlong,LI Qiuqiu   

  1. Faculty of Material and Photoelectronic Physics, Xiangtan University,Xiangtan Hunan 411105, China
  • Received:2013-07-22 Revised:2013-09-15 Online:2014-01-01 Published:2014-02-14
  • Contact: FENG Junpeng

摘要: 针对人脸识别系统准确度不高的问题,提出一种基于非下采样Contourlet梯度方向直方图(HNOG)的人脸识别算法。先对人脸图像进行非下采样Contourlet变换(NSCT),并将变换后的各系数矩阵进行分块,再计算各分块的梯度方向直方图(HOG),将所有分块的直方图串接得到人脸图像HNOG特征,最后用多通道最近邻分类器进行分类。在YALE人脸库、ORL人脸库上和CAS-PEAL-R1人脸库上的实验结果表明,人脸的HNOG特征有很强的辨别能力,特征维数较小,且对光照、表情、姿态的变化具有较好的鲁棒性。

关键词: 非下采样Contourlet变换, 梯度方向直方图, 人脸识别, 最近邻分类器

Abstract: Concerning the low accuracy of face recognition systems, a face recognition algorithm based on Histograms of Nonsubsampled contourlet Oriented Gradient (HNOG) was proposed. Firstly, a face image was decomposed with Non-Subsampled Contourlet Transform (NSCT) and the coefficients were divided into several blocks. Then histograms of oriented gradient were calculated all over the blocks and used as face features. Finally, multi-channel nearest neighbor classifier was used to classify the faces. The experimental results on YALE , ORL and CAS-PEAL-R1 face databases show that the descriptor HNOG is discriminative, the feature dimension is small and the feature is robust to variations of illumination, face expression and position.

Key words: Non-Subsampled Contourlet Transform (NSCT), Histograms of Oriented Gradient (HOG), face recognition, nearest neighbor classifier

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