Comparing a novel model based on the transferable belief model with humans during the recognition of partially occluded facial expressions

Journal of Vision

Volumn 9, Issue 2, 2009, Pages

  Hammal, Zakia ^a   Arguin, Martin ^a   Gosselin, Frédéric ^a  

^a UNIVERSITÉ DE MONTRÉAL   (Canada)

Author keywords

Bubbles; Facial expressions classification; Facial features behavior; Transferable belief model

Indexed keywords

ARTICLE; COMPARATIVE STUDY; COMPUTER SIMULATION; FACIAL EXPRESSION; HUMAN; PATTERN RECOGNITION; PERCEPTION; PSYCHOLOGICAL MODEL;

COMPUTER SIMULATION; FACIAL EXPRESSION; HUMANS; MODELS, PSYCHOLOGICAL; PATTERN RECOGNITION, VISUAL; PERCEPTUAL MASKING;

EID: 67649185396     PISSN: None     EISSN: 15347362     Source Type: Journal    
DOI: 10.1167/9.2.22     Document Type: Article

References (50)

1. Adolphs, R., Tranel, D., Damasio, H., & Damasio, A. (1994). Impaired recognition of emotion in facial expressions following bilateral damage to the human amygdala. Nature, 372, 669-672. [PubMed]
2. Bassili, J. N. (1978). Facial motion in the perception of faces and of emotional expression. Journal of Experimental Psychology: Human Perception and Performance, 4, 373-379. [PubMed]
3. Bassili, J. N. (1979). Emotion recognition: The role of facial movement and the relative importance of upper and lower areas of the face. Journal of Personality and Social Psychology, 37, 2049-2058. [PubMed]
4. Black, M. J., & Yacoob, Y. (1997). Recognizing facial expression in image sequences using local parametrized models of image motion. Transactions on Computer Vision, 25, 23-48.
5. Boucher, J. D., & Ekman, P. (1975). Facial areas and emotional information. Journal of Communication, 25, 21-29. [PubMed]
6. Cohn, J. F. (2006). Foundations of human computing: Facial expression and emotion. Proceedings of ACM Multimodal Interfaces, 233-238.
7. Cohn, J. F., & Schmidt, K. L. (2004). The timing of facial motion in posed and spontaneous smiles. Journal of Wavelets, Multi-Resolution Information Processing, 2, 1-12.
8. Cohn-Kanade database (2000). http://vasc.ri.cmu.edu/sidb/html/face/facial%20expression.
9. Dailey, M., Cottrell, G. W., & Reilly, J. (2001). California facial expressions (cafe). Unpublished digital images. San Diego, CA: University of California.
10. Darwin, C. (1872). The expression of the emotions in man and animals. London: Murray.
11. Denoeux, T. (2008). Conjunctive and disjunctive combination of belief functions induced by nondistinct bodies of evidence. Artificial Intelligence, 172, 234-264.
12. Ekman, P. (1999). The handbook of cognition and emotion: Facial expression. John Wiley and Sons.
13. Ekman, P., & Friesen, W. V. (1978). The facial action coding system (facs): A technique for the measurement of facial action. Palo Alto, CA: Consulting Psychologists Press.
14. Elouadi, Z., Mellouli, K., & Smets, P. (2004). Assessing sensor reliability for multisensor data fusion within the transferable belief model. IEEE Transactions on Systems, Man, and Cybernetics B, 34, 782-787. [PubMed]
15. Fasel, B., & Luettin, J. (2003). Automatic facial expression analysis: A survey. Pattern Recognition, 1, 259-275.
16. Gosselin, F., & Schyns, P. G. (2001). Bubbles: A technique to reveal the use of information in recognition tasks. Vision Research, 41, 2261-2271. [PubMed]
17. Gouta, K., & Miyamoto, M. (2000). Facial areas and emotional information. Japanese Journal of Psychology, 71, 211-218.
18. Hammal-Caplier database (2003). http://viscog.psy. umontreal.ca/zakia/index.htm.
19. Hammal, Z. (2006a). Facial features segmentation, analysis and recognition of facial expressions using the transferable belief model. PhD thesis, LIS Laboratory, Grenoble, France.
20. Hammal, Z. (2006b). Dynamic facial expression understanding based on temporal modeling of transferable belief model. Proceedings of the International Conference on Computer Vision Theory and Application, Setubal, Portugal, February 25-28.
21. Hammal, Z., Caplier, A., & Rombaut, M. (2005). A fusion process based on belief theory for classification of facial basic emotions. Proceedings of the 8th International Conference on Information Fusion (ICIF), Philadelphia, PA, USA.
22. Hammal, Z., Couvreur, L., Caplier, A., & Rombaut, M. (2007). Facial expressions classification: A new approach based on Transferable Belief Model. International Journal of Approximate Reasoning, 46, 542-567.
23. Hammal, Z., Eveno, N., Caplier, A., & Coulon, P. Y. (2006). Parametric models for facial features segmentation. Signal processing, 86, 399-413.
24. Izard, C. E. (1971). The face of emotion. New York: Appleton-Century-Crofts.
25. Izard, C. E. (1994). Innate and universal facial expressions: Evidence from developmental and cross-cultural research. Psychological Bulletin, 115, 288-299. [PubMed]
26. Lien, J. J., Kanade, T., Cohn, J. F., & Li, C. (1998). Subtly different facial expression recognition and expression intensity estimation. Proceedings of Computer Vision and Pattern Recognition (CVPR) (pp. 853-859). Santa Barbara, CA: IEEE.
27. Lucas, B. D., & Kanade, T. (1981). An iterative image-registration technique with an application to stereo vision. In Image Understanding Workshop, (pp. 121-130). US Defense Advanced Research Projects Agency.
28. Malciu, M., & Preteux, F. (2001). Mpeg-4 compliant tracking of facial features in video sequences. Proceedings of the International Conference on Augmented, Virtual Environments and 3D Imaging (pp. 108-111), Mykonos, Greece, May.
29. Mercier, D., Deunoeux, T., & Masson, M. H. (2006). Refined sensor tuning in the belief function framework using contextual discounting. Proceedings of Information Processing and Management of Uncertainty in Knowledge-Based Systems (vol. 2, pp. 1443-1450). Paris, France.
30. Pantic, M., & Bartlett, M. S. (2007). Machine analysis of facial expressions. In K. Delac & M. Grgic (Eds.), Face recognition (pp. 377-416). Vienna, Austria: ITech Education and Publishing.
31. Pantic, M., & Patras, I. (2005). Detecting facial actions and their temporal segmentation in nearly frontalview face image sequences. Proceedings of the IEEE International Conference on Systems Man and Cybernetics, Waikoloa, Hawaii, October.
32. Pantic, M., & Patras, I. (2006). Dynamics of facial expression: Recognition of facial actions and their temporal segments from face profile image sequences. IEEE Transactions on System Systems, Man, and Cybernetics, Part B: Cybernetics, 36, 433-449.
33. Pantic, M., & Rothkrantz, L. J. M. (2000a). Expert system for automatic analysis of facial expressions. Image and Vision Computing Journal, 18, 881-905.
34. Pantic, M., & Rothkrantz, L. J. M. (2000b). Automatic analysis of facial expressions: The state of the art. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 1424-1445.
35. Pards, M., & Bonafonte, A. (2002). Facial animation parameters extraction and expression detection using hmm. Signal Processing, Image Communication, 17, 675-688.
36. Rosenblum, M., Yacoob, Y., & Davis, L. S. (1996). Human expression recognition from motion using a radial basis function network architecture. IEEE Transactions on Neural Networks, 7, 1121-1137.
37. Roy, S., Roy, C., Fortin, I., Either-Majcher, C., Belin, P., & Gosselin, F. (2007). A dynamic facial expression database. Proceedings of the Vision Sciences Society, Sarasota, Florida.
38. Schmidt, K. L., Ambadar, Z., Cohn, J. F., & Reed, I., (2006, March). Movement differences between delib-erate and spontaneous facial expressions: Zygomaticus major action in smiling. Journal of Nonverbal Behavior, 30, 37-52.
39. Smets, P. (1998). The transferable belief model for quantified belief representation. Handbook of defeasible reasoning and uncertainty management system (vol. 1, pp. 267-301). Dordrecht: Kluwer Academic.
40. Smets, P. (2000). Data fusion in the transferable belief model. Proceedings of the International Conference on Information Fusion (pp. 21-33), Paris, France: IEEE.
41. Smets, P., & Kruse, R. (1994). The transferable belief model. Artificial Intelligence, 66, 191-234.
42. Smets, Ph. (2005). Decision making in the TBM: The necessity of the pignistic transformation. International Journal of Approximate Reasoning, 38, 133-147.
43. Smith, M., Cottrell, G., Gosselin, F., & Schyns, P. G. (2005). Transmitting and decoding facial expressions of emotions. Psychological Science, 16, 184-189.
44. Tekalp, M., & Ostermann, J. (2000). Face and 2-D mesh animation inMPEG-4. Image Communication Journal, 15, 387-421.
45. Tian, Y., Kanade, T., & Cohn, J. F. (2001). Recognizing action units for facial expression analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23, 97-115.
46. Tsapatsoulis, N., Karpouzis, K., Stamou, G., Piat, F., & Kollias, S. (2000). A fuzzy system for emotion classification based on the mpeg-4 facial definition parameter set. Proceedings of the 10th European Signal Processing Conference, Tampere, Finland, September 5-8.
47. Yacoob, Y., & Davis, L. S. (1996, June). Recognizing human facial expressions from long image sequences using optical flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 636-642.
48. Young, A. W., Rowland, D., Calder, A. J., Etcoff, N. L., Seth, A., & Perrett, D. I. (1997). Facial expression megamix: Tests of dimensional and category accounts of emotion recognition. Cognition, 63, 271-313. [PubMed]
49. Zeng, Z., Pantic, M., Roisman, G. I., & Huang, T. S. (2009). A survey of affect recognition methods: Audio, visual, and spontaneous expressions. Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence, 31, 39-58. [PubMed]
50. Zhang, Y., & Qiang, J. (2005, May). Active and dynamic information fusion for facial expression understanding from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27, 699-714.