Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Image Recognition by Using Hybrid Coefficient Measure of Correlation and Distance

상관계수과 거리계수의 조합형 척도를 이용한 영상인식

  • Hong, Seong-Jun (School of Computer and Information Comm. Eng., Catholic Univ. of Daegu) ;
  • Cho, Yong-Hyun (School of Computer and Information Comm. Eng., Catholic Univ. of Daegu)
  • 홍성준 (대구가톨릭대학교 컴퓨터정보통신공학부) ;
  • 조용현 (대구가톨릭대학교 컴퓨터정보통신공학부)
  • Received : 2010.04.03
  • Accepted : 2010.05.13
  • Published : 2010.06.25
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents an efficient image recognition method using the hybrid coefficient measure of correlation and distance. The correlation coefficient is applied to measure the statistical similarity by using Pearson coefficient, and distance coefficient is also applied to measure the spacial similarity by using city-block. The total similarity among images is calculated by extending the similarity between the feature vectors, then the feature vectors can be extracted by PCA and ICA, respectively. The proposed method has been applied to the problem for recognizing the 960(30 persons * 4 expressions * 2 lights * 4 poses) facial images of 40*50 pixels. The experimental results show that the proposed method of ICA has a superior recognition performances than the method using PCA, and is affected less by the environmental influences so as lighting.

본 논문에서는 상관계수와 거리계수의 조합형 유사성 척도에 기반을 둔 효과적인 영상인식 방법을 제안하였다. 여기서 상관계수는 Pearson coefficient에 의한 통계적 유사성을 측정하기 위함이고, 거리계수는 city-block에 의한 공간적인 유사성을 측정하기 위함이다. 또한 영상사이의 전체 유사성은 각 영상이 가지는 특징사이의 유사성으로 계산되며, 영상의 특징은 PCA와 ICA로 각각 추출하였다. 제안된 방법을 40*50 픽셀의 960(30명*4표정*2조명*4포즈)개 다른 표정영상을 대상으로 실험한 결과, ICA 기반 조합형 척도를 이용하는 것이 PCA 기반 조합형 척도보다 우수한 인식률을 가지며, 또한 조명과 같은 주변 환경에도 강건한 인식성능이 있음을 확인하였다.

Keywords

References

  1. A. Kapoor, Q. Yuan and R. W. Picard, "Fully Automatic Upper Facial Action Recognition", IEEE. Int. Analysis and Modelling of Faces and Gestures Workshop, pp. 195-202, 2003.
  2. Y. H. Cho and S. J. Hong, "Face Recognition by Using Combined Method of Centroid Shift and Fixed-Point ICA Based on Secant Method", Proc. of 6th Int. Conf. on Intelligent System Design and Applications, Oct. 2006.
  3. K. I. Diamantaras and S. Y. Kung, Principal Component Neural Networks : Theory and Applications, Adaptive and learning Systems for Signal Processing, Communications, and Control, John Wiley & Sons, Inc., 1996
  4. B. A. Draper, K. Baek, M. S. Barlett, and J. R. Beveridge, " Recognizing Faces with PCA and ICA," Computer Vision and Imaging Understanding 91, pp.115-137, 2003 https://doi.org/10.1016/S1077-3142(03)00077-8
  5. A. Hyvarinen and E. Oja, "A Fast Fixed Point Algorithms for Independent Component Analysis", Neural Computation, Vol. 9, No. 7, pp. 1483-1492, Oct. 1997. https://doi.org/10.1162/neco.1997.9.7.1483
  6. R. M. Aarts, R. Irwan, A. J. E. M. Janssen, "Efficient Tracking of the Cross-correlation Coefficient," IEEE. Trans. on Speech Audio Process, Vol. 10, No. 6, pp.381-402, 2002
  7. J. P. Lewis,"Fast Normalized Cross-Correlation," Available from http://www.idiom.com/
  8. F. Zhao, Q. Huang, and W. Gao," Image Matching by Normalized Cross-Correlation," Available from http://jdl.ac.cn/doc/2006/
  9. H. S. Lee et. al., “The POSTECH Face Database (PF07) and Performance Evaluation,” In Proceeding of the 8th IEEE International Conference on Automatic Face and Gesture Recognition, 2008

Cited by

  1. Meter Numeric Character Recognition Using Illumination Normalization and Hybrid Classifier vol.24, pp.1, 2014, https://doi.org/10.5391/JKIIS.2014.24.1.071