Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Real-time Recognition System of Facial Expressions Using Principal Component of Gabor-wavelet Features

표정별 가버 웨이블릿 주성분특징을 이용한 실시간 표정 인식 시스템

  • Received : 2009.09.01
  • Accepted : 2009.11.27
  • Published : 2009.12.25
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Abstract

Human emotion can be reflected by their facial expressions. So, it is one of good ways to understand people's emotions by recognizing their facial expressions. General recognition system of facial expressions had selected interesting points, and then only extracted features without analyzing physical meanings. They takes a long time to find interesting points, and it is hard to estimate accurate positions of these feature points. And in order to implement a recognition system of facial expressions on real-time embedded system, it is needed to simplify the algorithm and reduce the using resources. In this paper, we propose a real-time recognition algorithm of facial expressions that project the grid points on an expression space based on Gabor wavelet feature. Facial expression is simply described by feature vectors on the expression space, and is classified by an neural network with its resources dramatically reduced. The proposed system deals 5 expressions: anger, happiness, neutral, sadness, and surprise. In experiment, average execution time is 10.251 ms and recognition rate is measured as 87~93%.

표정은 인간의 감정을 전달할 수 있는 중요한 수단으로 표정 인식은 감정상태를 알아낼 수 있는 효과적인 방법중 하나이다. 일반적인 표정 인식 시스템은 얼굴 표정을 표현하는 특징점을 찾고, 물리적인 해석 없이 특징을 추출한다. 하지만 특징점 추출은 많은 시간이 소요될 뿐 아니라 특징점의 정확한 위치를 추정하기 어렵다. 그리고 표정 인식 시스템을 실시간 임베디드 시스템에서 구현하기 위해서는 알고리즘을 간략화하고 자원 사용량을 줄일 필요가 있다. 본 논문에서 제안하는 실시간 표정 인식 시스템은 격자점 위치에서 얻어진 가버 웨이블릿(Gabor wavelet) 특징 기반 표정 공간을 설정하고, 각 표정 공간에서 얻어진 주성분을 신경망 분류기를 이용하여 얼굴 표정을 분류한다. 제안하는 실시간 표정 인식 시스템은 화남, 행복, 평온, 슬픔 그리고 놀람의 5가지 표정이 인식 가능하며, 다양한 실험에서 평균 10.25ms의 수행시간, 그리고 87%~93%의 인식 성능을 보였다.

Keywords

References

  1. 심귀보, 박창현, '음성인식으로 감정인식 요소 분석,' 퍼지 및 지능 시스템학회 논문지, 11권, 11호, pp.510-515, 2001
  2. 고현주, 이대종, 박장환, 전명근, '웨이블릿 변환을 이용한 음성에서의 감정 추출 및 인식 기법,' 퍼지 및 지능 시스템학회 논문지, 14권, 2호, pp.150-155, 2004
  3. Andreas Haag, Silke Goronzy, Peter Schaich and Jason Williams, 'Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System,' In Proceedings of LNCS, Vol.3068, pp.36-48, 2004 https://doi.org/10.1007/978-3-540-24842-2_4
  4. Shishir Bashyala, Ganesh K. Venayagamoorthy, 'Recognition of facial expressions using Gabor wavelets and learning vector quantization,' Engineering Applications of Artificial Intelligence, Vol.21, No.7, pp.1056-1064, 2008 https://doi.org/10.1016/j.engappai.2007.11.010
  5. Pantic M, Rothkrantz L.J.M, 'Automatic analysis of facial expressions: the state of the art,' IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.22, No.12, pp.1424-1445, 2000 https://doi.org/10.1109/34.895976
  6. B. Fasel, J. Luettin, Beat Fasel, Juergen Luettin, 'Automatic Facial Expression Analysis: A Survey,' Pattern Recognition, Vol.36, pp.259-275, 1999 https://doi.org/10.1016/S0031-3203(02)00052-3
  7. C. Padgett, G.W. Cottrell, 'Representing face images for emotion classification,' Proc. Conf. Advances in Neural Information Processing Systems, Vol.9, pp.894-900, 1997
  8. Geng Xue, Zhang Youwei, 'Facial Expression Recognition Based on the Difference of Statistical Features,' International Conference on Signal Processing 2006, Vol.3, pp.16-20, 2006
  9. Matthew N. Dailey, Garrison W. Cottrell, Curtis Padgett, and Ralph Adolphs, 'EMPATH: A Neural Network that Categorizes Facial Expressions,' Journal of cognitive neuroscience, Vol.14, pp.1158-1173, 2002 https://doi.org/10.1162/089892902760807177
  10. 정성욱, 김도형, 안광호, 정명진, '실시간 얼굴 표정 인식을 위한 새로운 사각 특징 형태 선택기법', 제어자동화시스템공학회지, 12권, 2호, pp.130-137, 2006
  11. Michael J. Black, Yaser Yacoob, 'Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion,' International Journal of Computer Vision, Vol.25, No.1, pp.23-48, 2004
  12. Bouchra Abboud, Franck Davoine, Mo Dang, 'Facial expression recognition and synthesis based on an appearance model,' Signal Processing: Image Communication, Vol.19, No.8, pp.723-740, 2004 https://doi.org/10.1016/j.image.2004.05.009
  13. Yeongjae Cheon, Daijin Kim, 'A Natural Facial Expression Recognition Using Differential-AAM and k-NNS,' Pattren Recognition, Vol.42, No.7, pp.1340-1350, 2008
  14. Gokturk. S.B, Bouguet. J.-Y, Tomasi. C, Girod. B, 'Model-based face tracking for view-independent facial expression recognition,' IEEE International Conference on Automatic Face and Gesture Recognition, pp.287-293, 2002
  15. Viola P., Jones M., 'Rapid object detection using a boosted cascade of simple features,' Computer Vision and Pattern Recognition 2001, Vol.1 pp.511-518, 2001
  16. Yoav Freund, Robert E. Schapire, 'A decision-theoretic generalization of on-line learning and an application to boosting,' In Computational Learning Theory: Eurocolt' 95, pp.23-37, 1995
  17. P. Ekman, W. Friesen, 'Facial Action Coding System: A Technique for the Measurement of Facial Movement,' Consulting Psychologists Press, 1978
  18. Jie Zou, Qiang Ji, G. Nagy, 'A Comparative Study of Local Matching Approach for Face Recognition,' IEEE Transactions on Image Processing, Vol.16, No.10, pp.2617-2628, 2007 https://doi.org/10.1109/TIP.2007.904421
  19. I.T. Jollie, 'Principal Components Analysis,' Springer-verlag New York, 1986
  20. The Chinese Academy Sciences-Pose, Expression, Accessories, and Lighting(CAS-PEAL) Database http://www.jdl.ac.cn/peal/index.html
  21. The Japanese Female Facial Expression(JAFFE) Database http://www.kasrl.org/jaffe.html
  22. The Postech Faces' 01 Database http://nova.postech.ac.kr/special/imdb/imdb.html

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