Abstract：An embedded hidden markov model (e-HMM) based approach for facial expression recognition is proposed. It makes use of an optimized set of observation vectors obtained from the 2D-DCT coefficients of the facial region of interest. The e-HMM is trained with segmental K-means algorithm and used for the facial expression recognition. The experimental results show the remarkable improvement of the performance and robustness of the facial expression recognition system.
[1] Picard R W. Affective Computing. London, UK: MIT Press, 1997 [2] Ahn H, Picard R W. Affective-Cognitive Learning and Decision Making: The Role of Emotions // Proc of the 18th European Meeting on Cybernetics and Systems Research. Vienna, Austria, 2006: 18-19 [3] Ekman P. Facial Expression and Emotion. American Psychologist, 1993, 48(4): 384-392 [4]Ekman P, Friesen W V. Constants across Cultures in the Face and Emotion. Journal of Personality and Social Psychology, 1971, 17(2): 124-129 [5] Ekman P, Friesen W V. Facial Action Coding System: Investigator’s Guide. Palo Alto, USA: Consulting Psychologists Press, 1978 [6] Suwa M, Sugie N, Fujimora K. A Preliminary Note on Pattern Recognition of Human Emotional Expression // Proc of the 4th International Joint Conference on Pattern Recognition. Kyoto, Japan, 1978: 408-410 [7] Essa I A, Pentland A P. Coding, Analysis, Interpretation and Recognition of Facial Expression. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997, 19(7): 757-763 [8] Black M J, Yacoob Y. Recognizing Facial Expressions in Image Sequences Using Local Parameterized Models of Image Motion. International Journal of Computer Vision, 1997, 25(1): 23-48 [9] Yacoob Y, Davis L S. Recognizing Human Facial Expression from Long Image Sequences Using Optical Flow. IEEE Trans on Pattern Analysis and Machine Intelligence, 1996, 16(6): 636-642 [10] Rabiner L R. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proc of the IEEE, 1989, 77(2):257-286 [11] Nefian A V, Hayes M H. Hidden Markov Model for Recognition // Proc of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Las Vegas, USA, 1998, Ⅴ: 2721-2724 [12] Samaria F. Face Recognition Using Hidden Markov Models. Ph.D Dissertation. Cambridge, UK: University of Cambridge. Trinity College, 1994 [13] Otsuka T, Ohya J. Recognizing Multiple Persons′ Facial Expressions Using HMM Based on Automatic Extraction of Significant Frames from Image Sequences // Proc of the International Conference on Image Processing. Santa Barbara, USA, 1997, Ⅱ: 546-549 [14] Kuo S S, Agazzi O E. Keyword Spotting in Poorly Printed Documents Using Psedo 2D Hidden Markov Models. IEEE Trans on Pattern Analysis and Machine Intelligence, 1994, 16(8): 842-848 [15] Xiong Zixiang, Guleryuz G O, Orchard M T. A DCT-Based Embedded Image Coder. IEEE Signal Processing Letters, 1996, 3(11): 289-290 [16] Kohir V V, Desai U B. Face Recognition Using a DCT-HMM Approach // Proc of the 4th IEEE Workshop on Applications of Computer Vision. Princeton, USA, 1998: 226-231 [17] Russell M, Moore R. Explicit Modeling of State Occupancy in Hidden Markov Models for Automatic Speech Recognition // Proc of the IEEE International Conference on Acoustics, Speech, and Signal Processing. Tampa, USA, 1985: 5-8 [18] Buciu I, Pitas I. Application of Non-Negative and Local Non-Negative Matrix Factorization to Facial Expression Recognition // Proc of the 17th International Conference on Pattern Recognition. Cambridge, UK, 2004, Ⅰ: 288-291
2008, Vol. 21 
