Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
권호 표지
DOI QR코드

DOI QR Code

Robust Facial Expression Recognition Based on Signed Local Directional Pattern

Signed Local Directional Pattern을 이용한 강력한 얼굴 표정인식

  • Received : 2014.03.03
  • Accepted : 2014.05.28
  • Published : 2014.06.25
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we proposed a new local micro pattern, Signed Local Directional Pattern(SLDP). SLDP uses information of edges to represent the face's texture. This can produce a more discriminating and efficient code than other state-of-the-art methods. Each micro pattern of SLDP is encoded by sign and its major directions in which maximum edge responses exist-which allows it to distinguish among similar edge patterns that have different intensity transitions. In this paper, we divide the face image into several regions, each of which is used to calculate the distributions of the SLDP codes. Each distribution represents features of the region and these features are concatenated into a feature vector. We carried out facial expression recognition with feature vectors and SVM(Support Vector Machine) on Cohn-Kanade and JAFFE databases. SLDP shows better classification accuracy than other existing methods.

본 논문에서는 얼굴 표정인식을 위한 새로운 지역 미세 패턴 기술 방법인 Signed Local Directional Pattern(SLDP)을 제안한다. SLDP는 얼굴 영상의 텍스쳐 정보를 표현하기 위해 에지 정보를 이용한다. 이는 기존의 방법들에 비해 뛰어난 구별 성능과 효율적인 코드 생성을 가능하게 한다. SLDP는 마스크 범위 이웃 화소들을 이용하여 에지 반응 값을 계산하고 이들 중 부호를 고려하여 에지 반응 값이 큰 에지 방향 정보를 가지고 만들어진다. 이는 기존 LDP에서 구별하지 못하던 비슷한 에지구조에 밝기 값이 반대인 지역 패턴을 구별할 수 있다. 본 논문에서는 얼굴 표정인식을 위해 얼굴 영상을 여러 영역으로 분할하고 각 영역으로부터 SLDP코드의 분포를 계산한다. 각 분포는 얼굴의 지역적인 특징을 나타내고 이들 특징을 연결해서 얼굴 전체를 나타내는 얼굴 특징 벡터를 생성한다. 본 논문에서는 생성된 얼굴 특징 벡터와 SVM(Support Vector Machine)을 이용해서 Cohn-Kanade 데이터베이스와 JAFFE데이터베이스에서 얼굴 표정인식을 수행했다. SLDP는 표정인식에서 기존 방법들보다 뛰어난 결과를 보여주었다.

Keywords

References

  1. Y.L. Tian et al., Real World Real-time Automatic Recognition of Facial Expressions, Proc. IEEE Workshop Performance Evaluation of Tracking and Surveillance, 2003.
  2. C. Shan, S. Gong, and P.W. McOwan, "Robust Facial Expression Recognition using Local Binary Patterns," Proc. IEEE Int. Conf. Image Process., pp. 914-917, Genoa, Italy, September 2005.
  3. M.C. Hwang et al., "Person Identification System for Future Digital TV with Intelligence," IEEE Trans. Consum. Electron., vol. 53, no. 1, pp. 218-226, 2007. https://doi.org/10.1109/TCE.2007.339528
  4. P. Corcoran et al., "Biometric Access Control for Digital Media Streams in Home Networks," IEEE Trans. Consum. Electron., vol. 53, no. 3, pp. 917-925, 2007. https://doi.org/10.1109/TCE.2007.4341566
  5. C. Shan, S. Gong, and P.W. McOwan, "Facial Expression Recognition based on Local Binary Patterns: A Comprehensive Study," Image Vision Comput., vol. 27, no. 6, pp. 803-816, May 2009. https://doi.org/10.1016/j.imavis.2008.08.005
  6. Y. Tian, T. Kanade, and J.F. Cohn, "Facial Expression Analysis," Handbook of Face Recognition, Springer, pp. 247-275, Oct. 2003.
  7. M.A. Turk and A.P. Pentland, "Face Recognition Using Eigenfaces," Proc. Comput. Vision Pattern Recog., pp. 586-591, 1991.
  8. C. Padgett and G. Cottrell, "Representing Face Images for Emotion Classification," Advances in Neural Information Processing Systems, M. Mozer, M. Jordan, and T. Petsche, eds., vol. 9, Cambridge, Mass.: MIT Press, 1997.
  9. M.S. Bartlett, J.R. Movellan, and T.J. Sejnowski, "Face Recognition by Independent Component Analysis," IEEE Trans. Neural Networks, vol. 13, no. 6, pp. 1450-1464, 2002. https://doi.org/10.1109/TNN.2002.804287
  10. C.C. Fa and F.Y. Shin, "Recognizing Facial Action Units using Independent Component Analysis and Support Vector Machine," Pattern Recog., vol. 39, no. 9, pp. 1795-1798, 2006. https://doi.org/10.1016/j.patcog.2006.03.017
  11. M.J. Lyons, J. Budynek, and S. Akamatsu, "Automatic Classification of Single Facial Images," IEEE Trans. Pattern Anal. Mach. Intell., vol. 21, no. 12, pp. 1357-1362, 1999. https://doi.org/10.1109/34.817413
  12. T. Ojala and M. Pietikainen, "Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, 2002. https://doi.org/10.1109/TPAMI.2002.1017623
  13. X. Feng, M. Pietikainen, and A. Hadid, "Facial Expression Recognition with Local Binary Patterns and Linear Programming," Pattern Recog. Image Anal., vol. 15, no. 2, pp. 546-548, 2005.
  14. G. Zhao and M. Pietikainen, "Dynamic Texture Recognition using Local Binary Patterns with An Application to Facial Expressions," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 6, pp. 915-928, 2007. https://doi.org/10.1109/TPAMI.2007.1110
  15. H. Zhou, R. Wang, and C. Wang, "A Novel Extended Local Binary Pattern Operator for Texture Analysis," Inf. Science, vol. 178, no. 22, pp. 4314-4325, 2008. https://doi.org/10.1016/j.ins.2008.07.015
  16. T. Jabid, M.H. Kabir, and O.S. Chae, "Local Directional Pattern (LDP) for Face Recognition," IEEE Int. Conf. Consum. Electron., pp. 329-330, Las Vegas, U.S.A, Jan. 2010.
  17. T. Kanade, J. Cohn, and Y. Tian, "Comprehensive Database for Facial Expression Analysis," IEEE Int. Conf. Autom. Face Gesture Recog., pp. 46-53, Grenoble, France, Mar. 2000.
  18. W.K. Pratt, Digital Image Processing, Wiley, New York, pp. 489, 1978.
  19. T. Jabid, M.H. Kabir, and O.S. Chae, "Local Directional Pattern (LDP): A Robust Image Descriptor for Object Recognition," IEEE Int. Conf. Adv. Video and Signal-Based Surveillance, pp. 482-487, Boston, U.S.A, Sep. 2010.
  20. T. Ahonen, A. Hadid, and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037-2041, 2006. https://doi.org/10.1109/TPAMI.2006.244
  21. S. Gundimada and V.K. Asari, "Facial Recognition Using Multisensor Images Based on Localized Kernel Eigen Spaces,"IEEE Trans. Image Process., vol. 18, no. 6, pp. 1314-1325, 2009. https://doi.org/10.1109/TIP.2009.2016713
  22. C. Cortes and V. Vapnik, "Support Vector Networks," Machine Learning, vol. 20, no. 3, pp. 273-297, 1995.
  23. Z. Niu et al., "2D Cascaded AdaBoost for Eye Localization," Proc. IEEE Int. Conf. Pattern Recog., pp. 1216-1219, Hong Kong, Aug. 2006.
  24. W. Zhang, S. Shan, W. Gao, X. Chen, and H. Zhang, "Local Gabor Binary Pattern Histogram Sequence (LGBPHS): A Novel Non-Statistical Model for Face Representation and Recognition," Proc. IEEE Int'l Conf. Computer Vision, pp. I: 786-791, Beijing, China, Oct. 2005.
  25. T. Jabid, M. H. Kabir, and O. Chae, "Robust facial expression recognition based on local directional pattern," ETRI Journal, vol. 32, pp. 784-794, 2010. https://doi.org/10.4218/etrij.10.1510.0132
  26. P. Phillips, H. Moon, S. Rizvi, and P. Rauss,"The FERET evaluation methodology for face-recognition algorithms," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 10, pp. 1090-1104, Oct. 2000. https://doi.org/10.1109/34.879790
  27. M.S. Bartlett et al., "Recognizing Facial Expression: Machine Learning and Application to Spontaneous Behavior," IEEE Conf. Computer Vision and Pattern Recog., pp. 568-573, San Diego, CA. U.S.A, June 2005.