반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
권호 표지

대각선형 지역적 이진패턴을 이용한 성별 분류 방법에 대한 연구

A Study on Gender Classification Based on Diagonal Local Binary Patterns

  • 최영규 (한국기술교육대학교 정보기술공학부) ;
  • 이영무 (지식경제부 우정사업본부)
  • 발행 : 2009.09.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Local Binary Pattern (LBP) is becoming a popular tool for various machine vision applications such as face recognition, classification and background subtraction. In this paper, we propose a new extension of LBP, called the Diagonal LBP (DLBP), to handle the image-based gender classification problem arise in interactive display systems. Instead of comparing neighbor pixels with the center pixel, DLBP generates codes by comparing a neighbor pixel with the diagonal pixel (the neighbor pixel in the opposite side). It can reduce by half the code length of LBP and consequently, can improve the computation complexity. The Support Vector Machine is utilized as the gender classifier, and the texture profile based on DLBP is adopted as the feature vector. Experimental results revealed that our approach based on the diagonal LPB is very efficient and can be utilized in various real-time pattern classification applications.

키워드

참고문헌

  1. B. Moghaddam and M. Yang, “Gender Classification with Support Vector Machines,” Proc. Int'l Conf. Automatic Face and Gesture Recognition, pp. 306-311, Mar. 2000.
  2. M. Castrillon-Santana, “On Real-Time Face Detection in Video Streams: An Opportunistic Approach,” PhD dissertation, Universidad de Las Palmas de Gran Canaria, Mar. 2003.
  3. E. Makinen and R. Raisamo, “Evaluation of Gender Classification Methods with Automatically Detected and Aligned Faces,” IEEE Trans. on PAMI, Vol. 30, No. 3, pp. 541-547, 2008 https://doi.org/10.1109/TPAMI.2007.70800
  4. G. Zhao and M. Pietikainen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,” IEEE Trans. on PAMI, Vol. 29, No. 6, pp. 915-928, 2007
  5. M. Heikkila and M. Pietikainen, “A Texture-Based Method for Modeling the Background and Detecting Moving Objects,” IEEE Trans. on PAMI, Vol. 28, No. 4, pp. 657-662, April 2006.
  6. P. Viola and M.J. Jones, “Robust Real-Time Face Detection,” Int'l J. Computer Vision, vol. 57, no. 2, pp. 137-154, 2004. https://doi.org/10.1023/B:VISI.0000013087.49260.fb
  7. “OpenCV 1.0, Open Source Computer Vision Library,” http://www.intel.com/technology/computing/opencv/, 2006.
  8. T. Cootes and C. Taylor, Statistical Models of Appearance for Medical Image Analysis and Computer Vision, 2001.
  9. E. Makinen and R. Raisamo, “Real-Time Face Detection for Kiosk Interfaces,” Proc. Asia-Pacific Conf. Computer-Human Interaction 2002, pp. 528-539, 2002.
  10. Y99999 Freund and R. Schapire, “A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,” J. Computer Systems Science, vol. 55, no. 1, pp. 119-139, 1997. https://doi.org/10.1006/jcss.1997.1504
  11. C. Cortes and V. Vapnik, “Support-Vector Networks,” Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
  12. C. Chang and C. Lin, “LIBSVM: A Library for Support Vector Machines,” http://www.csie.ntu.edu.tw/~cjlin/libsvm/, 2001.