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효과적인 얼굴 표정 인식을 위한 퍼지 웨이브렛 LDA융합 모델 연구

A Study on Fuzzy Wavelet LDA Mixed Model for an effective Face Expression Recognition

  • 노종흔 (원광대학교 전자공학과) ;
  • 백영현 (원광대학교 전자공학과) ;
  • 문성룡 (원광대학교 전자공학과)
  • 발행 : 2006.12.25

초록

본 논문에서는 퍼지 소속 함수와 웨이브렛 기저를 이용한 효과적인 얼굴 표정 인식 LDA 융합모델을 제안하였다. 제안된 알고리즘은 최적의 영상을 얻기 위해 퍼지 웨이브렛 알고리즘을 수행하고, 표정 검출은 얼굴 특징 추출단계와 얼굴표절인식 단계로 구성된다. 본 논문에서 얼굴 표정이 담긴 영상을 PCA를 적용하여 고차원에서 저차원의 공간으로 변환 후, LDA 특성을 이용하여 클래스 별호 특징벡터를 분류한다. LDA 융합 모델은 얼굴 표정인식단계는 제안된 LDA융합모델의 특징 벡터에 NNPC를 적응함으로서 얼굴 표정을 인식한다. 제안된 알고리즘은 6가지 기본 감정(기쁨, 화남, 놀람, 공포, 슬픔, 혐오)으로 구성된 데이터베이스를 이용해 실험한 결과, 기존알고리즘에 비해 향상된 인식률과 특정 표정에 관계없이 고른 인식률을 보임을 확인하였다.

In this paper, it is proposed an effective face expression recognition LDA mixed mode using a triangularity membership fuzzy function and wavelet basis. The proposal algorithm gets performs the optimal image, fuzzy wavelet algorithm and Expression recognition is consisted of face characteristic detection step and face Expression recognition step. This paper could applied to the PCA and LDA in using some simple strategies and also compares and analyzes the performance of the LDA mixed model which is combined and the facial expression recognition based on PCA and LDA. The LDA mixed model is represented by the PCA and the LDA approaches. And then we calculate the distance of vectors dPCA, dLDA from all fates in the database. Last, the two vectors are combined according to a given combination rule and the final decision is made by NNPC. In a result, we could showed the superior the LDA mixed model can be than the conventional algorithm.

키워드

참고문헌

  1. P.Ekman and W.V. Friesen. 'Emotion in the human face System.' Cambridge University Press, San Francisco, CA, second edition, 1982
  2. Z. Zang, M. Lyons, M. Schuster and S. Akamatsu, 'Comparison between Geometry-Based and Gabor Wavelets-Based Facial Expression Recognition Using Multi-Layer Perceptron', Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, pp.454-459, 1998
  3. J.J. Lien, T. Kanade, J. Cohn, and C. Li, 'Detection, Tracking, and Classification of Action Units in Facial Expression', Journal of Robotics and Autonomous Systems, July, 1999
  4. M. Turk, A. Pentland, 'Eigenfaces for recognition', Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991 https://doi.org/10.1162/jocn.1991.3.1.71
  5. P. Belhumeur, J. Hespanha, D. Kriegman, 'Eigenfaces vs. fisherfaces: Recognition using class specific linear projection,' IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, 1997 https://doi.org/10.1109/34.598228
  6. Raghuveer M. Rao, Ajit S. Bopardika. 'Wavelet Transforms : Introduction to Theory & Applications', pp 3-8, Prentice Hall PTR, 1998
  7. Gian Luca Marcialis and Fabio Roli, 'Fusion of LDA and PCA for Face Verification', Proceeding of the Workshop on Biometric Authentication. M. Tistarelli and J. Bigun Eds.. Springer LNSC 2359, Copenhagen Denmark, 2002
  8. Geof H. Givens, J. Ross Beverideg. Bruce A. Dreaper and David Bolme. 'Using A Generalized Linear Mixed Model to Study the Configuration Space of a PCA+LDA Human Face Recognition Algorithm', Technical Report, Computer Science, 2003