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A Recognition Framework for Facial Expression by Expression HMM and Posterior Probability  

Kim, Jin-Ok (대구한의대학교 멀티미디어학부)
Publication Information
Journal of KIISE:Computing Practices and Letters / v.11, no.3, 2005 , pp. 284-291 More about this Journal
Abstract
I propose a framework for detecting, recognizing and classifying facial features based on learned expression patterns. The framework recognizes facial expressions by using PCA and expression HMM(EHMM) which is Hidden Markov Model (HMM) approach to represent the spatial information and the temporal dynamics of the time varying visual expression patterns. Because the low level spatial feature extraction is fused with the temporal analysis, a unified spatio-temporal approach of HMM to common detection, tracking and classification problems is effective. The proposed recognition framework is accomplished by applying posterior probability between current visual observations and previous visual evidences. Consequently, the framework shows accurate and robust results of recognition on as well simple expressions as basic 6 facial feature patterns. The method allows us to perform a set of important tasks such as facial-expression recognition, HCI and key-frame extraction.
Keywords
facial expression recognition; facial expression-classification; HMM; face detection;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 C. E. Shannon, 'A mathematical theory of communication,' Bell System Technical Journal, vol. 27, pp. 379-423 and 623-656, 1948
2 C. Choi, S. Kim, and W. Choi, 'Survivality Modeling for Quantitative Security Assessment in Ubiquitous Computing Systems,' Lecture Notes in Computer Science, Springer, Vol.3043, No.1, pp.207-214, May, 2004   DOI
3 Viterbi, A. J. (1967). Error bounds for convolution codes and an asymptotically optimal decoding algorithm. IEEE trans. on Information Theory, 12:260-269   DOI   ScienceOn
4 Y. Zhang, Q. Ji, 'Facial Expression Understanding in Image Sequences Using Dynamic and Active Visual Information Fusion,' IEEE Inter. Conf. on . Computer Vision (ICCV2003) , vol. 2, 2003
5 이경아, '웨이블렛 계수와 Hidden Markov Model를 이용한 얼굴인식 기법', 한국 퍼지 및 지능시스템학회 03년 추계 학술대회 학술발표 논문집, 162-165쪽, 2003   과학기술학회마을
6 L. R. Rabiner, 'A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,' Pro. IEEE, vol 77, no. 2, pp. 257-286, 1989   DOI   ScienceOn
7 C. Bregler, 'Learning and recognizing human dynamics in video sequences,' Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 568-574, 1998   DOI
8 Jin Ok Kim, Sung Jin Seo and Chin Hyun Chung, 'Real-Time Face Recognition by the PCA with Color Images,' Lecture Notes in Computer Science, LNCS 3036, pp. 687-690, June 2004
9 최명근, 정현숙, 신영숙, 이일병, '표정 변화에 따른 얼굴표정에서의 특징점 추적', 한국정보과학회 논문집 7권, 2호, 425-427쪽, 2000
10 T. Otsuka and J, Ohya, 'Spotting Segments Displaying Facial Expression from Image Sequences Using HMM,' In Proc. Inter. Conf. on Automatic Face and Gesture Recognition 98, pp. 442-477, 1998   DOI
11 J. J. Lien, H. Kanade, T. Kitamura, J. F. Cohn and C. C. Li, 'Detection, Tracking and Classification of Action Units in Facial Expression,' In Journal of Robotics and Autonomous Systems, pp. 432-329, 1998   DOI   ScienceOn
12 P. Ekman and W. V. Friesen, 'The Facial Action Coding System: A Technique for Measurement of Facial Movement,' Consulting Psychologists Press, San Francisco, CA, 1978
13 S. Muller, S. Eickerler and G. Rigoll, 'Pseudo 3D HMMs for Image Sequence Recognition,' in IEEE Proc. Inter. Conf. on Image Processing. 1999, pp. 237-241, 1999
14 J. Hoey, 'Hierarchical Unsupervised Learning of Facial Expression Categories,' IEEE Workshop on Detection and Recognition of Events in Video (EVENT'01), pp. 92-99, 2001   DOI
15 M. Pantic and L. Rothrkantz, 'Expert System for Automatic Analysis of Facial Expression,' J. Image and Vision Computing, vol. 18, no. 11, pp. 881-905, 2000   DOI   ScienceOn
16 B. Fasel, 'Multiscale Facial Expression Recognition using Convolutional Neural Networks,' In Proc. of the Third Indian Conference on Computer Vision, Graphics and Image Processing(ICVGIP'2002), 2002
17 M. N. Daile, W. C. Cottrell, C. Padgett and R. Adlophs, 'EMPATH: A Neural Network that Categorizes Facial Expressions,' Journal of Cognitive Science, vol. 14, no. 8, pp. 1158-1173, 2002   DOI   ScienceOn
18 M.J. Lyons, J. Budynek and S. Akamatsu, 'Automatic Classification of Single Facial Images,' IEEE Trans. Pattern Anal. Machine Intell., vol. 21, no. 12, pp. 1357-1362, 1999   DOI   ScienceOn
19 M. Schulze, K. Scheffeller and C. W. OmIin, 'Recognizing Facial Actions with Support Vector Machines,' In Proc. PRASA 2002, pp. 93-96, 2002
20 Essa, I., and Pentland, A., 'Coding, Analysis, Interpretation, and Recognition of Facial Expressions,' IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.19, pp.757-763, 1997   DOI   ScienceOn
21 M. Rosenblum, Y. Yacoob and L. S. Davis, 'Human expression recognition from motion using a radial basis function network architecture,' IEEE Transactions on Neural networks, Vol. 7, No.5, pp. 1121-1138, 1996   DOI   ScienceOn