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HOG在人脸识别中的性能研究 被引量：9

Performance Research of HOG in Face Recognition
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摘要 介绍梯度方向直方图(HOG)人脸识别算法,设计基于脸部识别技术人脸库的HOG人脸识别实验,以测试不同HOG参数对人脸识别的影响,从而进行最优参数设置。实验结果表明,HOG特征在行人检测和人脸识别上对梯度方向空间和区间的选择是一致的,不同分块模式对人脸识别的影响与行人检测不同,HOG特征描述子用较少的特征维数就能有效地表达人脸,采用块内标准化方式后,识别性能有大幅度提升。 This paper describes the face recognition algorithm of Histograms of Oriented Gradients（HOG）,and designs face recognition experiment based on HOG.The Experiment is done on the Face Recognition Technology（FERET） face database.It testes the effect of different HOG parameters on face recognition and tries to find the optimal parameter settings.Experimental results show that the choice of space and range in gradient direction of HOG feature is the same on pedestrian detection and face recognition.The different block mode has different effects too.HOG descriptor can express face effectively when it produce less characteristic dimension in non-overlapping manner.Recognition performance improves significantly when it is standardized.
作者 向征 谭恒良 马争鸣
机构地区 广东药学院医药信息工程学院 中山大学信息科学与技术学院
出处 《计算机工程》 CAS CSCD 2012年第15期194-196,200,共4页 Computer Engineering
基金 2010年度广东省教育部产学研结合基金资助项目(2010B090400013)
关键词 梯度方向直方图 人脸识别 行人检测 欧氏距离 Histograms of Oriented Gradients（HOG） face recognition pedestrian detection Euclidean distance
分类号 TP391 [自动化与计算机技术—计算机应用技术]
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参考文献10

  • 1Dalai N, Triggs B. Histograms of Oriented Gradients for Human Detection[C]//Proc. of CVP'05. Washington D. C., USA: IEEE Press, 2005: 886-893.
  • 2Deniz O, Bueno G, Torte L. Face Recognition Using Histograms of Oriented Gradients[J]. Pattern Recognition Letters, 32(12): 1598-1603.
  • 3叶林,陈岳林,林景亮.基于HOG的行人快速检测[J].计算机工程,2010,36(22):206-207. 被引量：16
  • 4宋金龙,胡福乔,赵宇明.基于Feature Forest的图像检索[J].计算机工程,2010,36(21):231-233. 被引量：2
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  • 7谭恒昆基于融合全局和局部HOG特征的人脸识别方法[D].广州:中山大学,2011.
  • 8Phillips P J, Syed H M, Rizvi A, et al. The FERET Evaluation Methodology for Face-recognition Algorithms[J]. IEEE Trans. on Pattern Analysis and Machine InteLligence, 2000, 22(10): 1090- 1104.
  • 9Su Yu, Shah Shiguang, Chen Xilin, et al. Hierarchical Ensemble of Global and Local Classifiers for Face Recognition[J]. IEEE Transactions on Image Processing, 2009, 18(8): 1885-1896.
  • 10Lowe D G. Distinctive Image Features from Scale-invariant Keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110.

二级参考文献11

  • 1Nister D, Stewenius H. Scalable Recognition with a Vocabulary Tree[C]//Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington D. C., USA: [s. n.]. 2006: 2161-2168.
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  • 7Oren M, Papageorgiou C, Sinha P, et al. Pedestrian Detection Using Wavelet Templates[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. [S. l.]: IEEE Press, 1997: 193-199.
  • 8Wu Ying, Yu Ting, Hua Gang. A Statistical Field Model for Pedestrian Detection[C]//Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. [S. l.]: IEEE Press, 2005: 1023-1030.
  • 9Bertozzi M, Brogi A, Caraffi C, et al. Pedestrian Detection by Means of Far-infrared Stereo Vision[J]. Computer Vision and Image Understanding, 2007, 106(2/3): 194-204.
  • 10Paul V, Jones M. Robust Real-time Face Detection[J]. International Journal of Computer Vision, 2004, 57(2): 137-154.

共引文献16

同被引文献88

  • 1浦剑,张军平,黄华.超分辨率算法研究综述[J].山东大学学报（工学版）,2009,39(1):27-32. 被引量：35
  • 2李武军,王崇骏,张炜,陈世福.人脸识别研究综述[J].模式识别与人工智能,2006,19(1):58-66. 被引量：107
  • 3Dalal N , Triggs B. Histograms of oriented gradients for human detection [ C ]. San Diego : Compute Vision and Pattern Recognition ,2005 : 886 - 893.
  • 4Cao Jia - heng , Song Lin. Scale Space Histogram of Oriented Gradients for Human Detection[ C]. Shanghai : International Symposium on Information Science and Engineering, 2008:167 -170.
  • 5Ojala T, Pietikainen M, Maenpaa T. Muhiresolution Gray- scale and Rotation Invariant Texture Classification with Local Binary Pattern[ J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2002, 24(7) : 971 -987.
  • 6Zhao Yang. Theories and Applications of LBP: A Survey[ C]. Zhengzhou: 7th International Conference on Intelligent Computing,2012 : 112 - 120.
  • 7Ahonen T, Hadid A, Pietikainen M. Face description with local binary patterns: Application to face recognition[ J ]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2006, 28 (12) : 2 037 -2 041.
  • 8TAN X, CHEN S, ZHOU Z, et al. Face recognition from a single image per person: a survey J]. Pattern Recognition, 2006, 39(9) : 1725 - 1745.
  • 9AHONEN T, HADID A, PIETIKAINEN M. Face de- scription with local binary patterns: application to face recognition [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(12) : 2037 - 2041.
  • 10CHEN D, CAO X, WEN F, et al. Higher is better: high-dimensional feature and its efficient compression for face verification [C]// IEEE Conference on Computer Vision and Pattern Recognition ( CVPR ' 13). Portland: IEEE Computer Society, 2013 : 1 - 8.

引证文献9

二级引证文献47

计算机工程
2012年 第15期

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