Research on Feature Matching of Multi-pose Face Based on SIFT

Автор: Yingjie Xia, Yanbin Han, Jinping Li, Rui Chen

Журнал: International Journal of Information Engineering and Electronic Business(IJIEEB) @ijieeb

Статья в выпуске: 1 vol.2, 2010 года.

Бесплатный доступ

Feature matching based on multi-pose faces has become more important in recent years. And it can be used in many fields, such as video monitoring, identity recognition, and so on. In this paper, SIFT algorithm is combined with AdaBoost algorithm, and a method of feature matching based on a multi-pose face is established. Firstly, the face region is extracted from multi-pose face images by AdaBoost. Secondly, SIFT characteristic vectors of the main regions are extracted and matched. The images of the ORL face DB are used in this paper, and some pictures taken in the experiment are used too. The matching results are acceptable and reasonable. Based on multi-pose face, it can be used to research face feature matching, face recognition, video monitoring and 3-D face reconstruction.

Еще

SIFT, feature matching, AdaBoost, multi-pose face

Короткий адрес: https://sciup.org/15013043

IDR: 15013043

Список литературы Research on Feature Matching of Multi-pose Face Based on SIFT

  • Liang Luhong, Ai Haizhou, Xu Gangyou, et al, “A Survey of Human Face Detection,” Chinese Journal of Computers, vol. 25, pp. 449-458, May 2002.
  • Qu Yanfeng, Li Weijun, Xu Jian, and Wang Shouju, “Fast Multi-Pose Face Detection in A Complex Background,” Journal Of Computer-Aided Design & Computer Graphics, vol. 16, pp. 45-50, January 2004.
  • CHEN Lei, HUANG Xianwu, and SUN Bing, “Pose-varied Face Recognition Based on WT and LVQ Network,” Computer Engineering, vol. 32, pp. 47-49, November 2006.
  • Zhu Changren, and Wang Runsheng, “Multi-Pose Face Recognition Based On A Hierarchical Model And Fusion Decision,” Journal Of Electronics And Information Technology, vol. 24, pp. 1447-1453, November 2002.
  • Zhu Changren, and Wang Runsheng, “Multi-Pose Face Recognition Based on a Single View,” Chinese Journal Of Computer, vol. 26, pp. 104-109, January 2003.
  • Zhu Changren, and Wang Runsheng, “Research On Multi-Pose Face Image Synthesis From A Single View,” Journal Of Electronics And Information Technology, vol. 25, pp. 300-305, March 2003.
  • BI Ping, ZHAO Heng, and LIANG Ji-min, “Variant Pose Face Detection Based on Multi-classifier Fusion,” Journal of System Simulation, vol. 21, pp. 6469-6473,6478, October 2009.
  • Liang Luhong, Ai Haizhou, He Kezhong, el al, “Face Detection Based on The Matching of Multiple Related Templates,” Chinese Journal of Software, vol. 12, pp. 94-102, January 2001.
  • SHAO Ping, YANG Lu-ming, HUANG Hai-bin, and ZENG Yao-rong, “Rapid Face Detection with Multi-pose Knowledge Models and Templates,” Journal of Chinese Computer Systems, vol. 28, pp. 346-350, February 2007.
  • CHEN Hua-jie, and WEI Wei, “Multi-pose Face Recognition Based on Correlative Sub-region Mapping,” Journal of Image and Graphic, vol. 12, pp. 1254-1260, July 2007.
  • Freund Y, “Boosting a weak learning algorithm by majority,” Information and Computation, vol. 121, pp. 256-285, February 1995.
  • P Viola , M Jones, “Robust Real-time Object Detection,” Second International Workshop on Statistical and Computational Theories of Vision-Modeling, Learning, Computing and Sampling, vol. 13, pp. 1-25, 2001.
  • Lowe D G, “Distinctive image features from Scale-Invariant keypoints,” International Journal of Computer Vision, vol. 60, pp. 91-110, February 2004.
  • Paul Viola, Michael Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, pp. 905-910, 2001.
  • Lindeberg T, “Scale-space theory: A basic tool for analyzing structures at different scales,” Journal of Applied Statistics, vol. 21, pp. 224-270, 1994.
Еще
Статья научная