Development of an Emotion Recognition Robot using a Vision Method

비전 방식을 이용한 감정인식 로봇 개발

  • Shin, Young-Geun (Department of Industrial Systems and Information Engineering, Korea University) ;
  • Park, Sang-Sung (Department of Industrial Systems and Information Engineering, Korea University) ;
  • Kim, Jung-Nyun (Department of Industrial Systems and Information Engineering, Korea University) ;
  • Seo, Kwang-Kyu (Department of Industrial Information and Systems Engineering, Sangmyung University) ;
  • Jang, Dong-Sik (Department of Industrial Systems and Information Engineering, Korea University)
  • 신영근 (고려대학교 산업시스템공학과) ;
  • 박상성 (고려대학교 산업시스템공학과) ;
  • 김정년 (고려대학교 산업시스템공학과) ;
  • 서광규 (상명대학교 산업정보시스템공학과) ;
  • 장동식 (고려대학교 산업시스템공학과)
  • Received : 20050800
  • Accepted : 20060800
  • Published : 2006.09.30

Abstract

This paper deals with the robot system of recognizing human's expression from a detected human's face and then showing human's emotion. A face detection method is as follows. First, change RGB color space to CIElab color space. Second, extract skin candidate territory. Third, detect a face through facial geometrical interrelation by face filter. Then, the position of eyes, a nose and a mouth which are used as the preliminary data of expression, he uses eyebrows, eyes and a mouth. In this paper, the change of eyebrows and are sent to a robot through serial communication. Then the robot operates a motor that is installed and shows human's expression. Experimental results on 10 Persons show 78.15% accuracy.

Keywords

References

  1. Y. Dai, Y. Nakano( 1996), Face-texture model-based on SGLD and its application in face detection in a color scene, Pattern Recognition, 29(6), 1007-1017 https://doi.org/10.1016/0031-3203(95)00139-5
  2. H. Wu, Q. Chen and M.Yachida(1999), Face Detection From Color Images Using Fuzzy Pattern Matching Method, IEEE Tran. on Pattern Analysis and Macbine Intelligence, 21(66), 557-563 https://doi.org/10.1109/34.771326
  3. K. Sobottka and I. Pitas(1997), Looking for faces and Facial Features in color images, Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications. Russian Academy of science. 7(1)
  4. H. A. Rowley and S. Baluja( 1998), Neural Network-Based Face Detection, IEEE Tran. on Pattern Analysis and Machine Intelligence, 20(1), 23-28 https://doi.org/10.1109/34.655647
  5. M. A. Turk, A. P. Pentland( 1991), Face recognition using eigenfaces, Proceedings of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 586-591
  6. J. Zhang, Y. Yan, M. Lades(1997), Face recognition: eigenface, elastic matching, and neural nets, Proceedings of the IEEE, 85(9).1423-1435
  7. D. Swets and J.Weng(1996), Using discriminant eigenfeatures for image retrieval, PAMl, 18(8),831-836 https://doi.org/10.1109/34.531802
  8. W. Zhao, R Chellappa, and N. Nandhakumar(1998), Empirical performance analysis of linear discriminant classifiers, In Proceedings of the 1998 Conference on Computer Vision and Pattern Recognition, 164-169
  9. K. Etemad and R Chellappa(1997), Discriminant analysis for recognition of human face images, Journal of the Optical Society of America A, 14(8), 1724-1733 https://doi.org/10.1364/JOSAA.14.001724
  10. Daewon. Kim(1998), VR Tech Memo 98-04, Virtual Rcality Laboratory, Dept. of Computer Science, KAlST
  11. Mark Rosenblum, Yaser Yacoob, and Larry S.Davis(1996), Human Expression Recognition from Motion using a Radial Basis Function Network Architecture, IEEE Transaction on Neural Network, 7(5),1121-1138 https://doi.org/10.1109/72.536309
  12. Christine Connolly and Thomas Fliess(1997), A Study of Efficiency and Accuracy in the Transformation from RGB to CIELAB Color Space, IEEE Transactins on Image Processing, 6(1), 1046-1048 https://doi.org/10.1109/83.597279
  13. Ira Cohen, Ashutosh Garg, Thomas S.Huang(2000), Emotion Recognition from Facial Expressions using Multilevel HMM Neural Information Processing Systems, Proc. NlPS Workshop on Affective Computing, Colorado
  14. Christopher J. C. Burges(1998), A tutorial on Support Veccor Machines for Pattern Recognition, Data Mining and Knowledge Discovery, 2,121-167 https://doi.org/10.1023/A:1009715923555