Abstract
Micro-expressions are one of the most important behavioral clues for lie and dangerous demeanor detections. However, it is difficult for humans to detect micro-expressions. In this paper, a new approach for automatic micro-expression recognition is presented. The system is fully automatic and operates in frame by frame manner. It automatically locates the face and extracts the features by using Gabor filters. GentleSVM is then employed to identify micro-expressions. As for spotting, the system obtained 95.83% accuracy. As for recognition, the system showed 85.42% accuracy which was higher than the performance of trained human subjects. To further improve the performance, a more representative training set, a more sophisticated testing bed, and an accurate image alignment method should be focused in future research.
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References
Ekman, P., Friesen, W.V.: Nonverbal Leakage and Clues to Deception. Psychiatry 32, 88–97 (1969)
Ekman, P.: Lie Catching and Microexpressions. In: Martin, C. (ed.) The Philosophy of Deception, pp. 118–133. Oxford University Press, Oxford (2009)
ten Brinke, L., MacDonald, S., Porter, S., O’ Conner, B.: Crocodile Tears: Facial, Verbal and Body Language Behaviors Associated with Genuine and Fabricated Remorse. Law. Hum. Behav., 1–11 (2011)
Ekman, P.: Telling Lies, 2nd edn. Norton, New York (2009)
Weinberger, S.: Intent to Deceive: Can the Science of Deception Detection Help to Catch Terrorists? Nature 465, 412–415 (2010)
Ekman, P.: Micro Expression Training Tool. University of California, San Francisco (2003)
Frank, M.G., Herbasz, M., Sinuk, K., Keller, A., Nolan, C.: I See How You Feel: Training Laypeople and Professionals to Recognize Fleeting Emotions. In: The Annual Meeting of the International Communication Association. Sheraton New York, New York City (2009), http://www.allacademic.com/meta/p15018_index.html
Polisovsky, S., Kameda, Y., Ohta, Y.: Facial Micro-Expressions Recognition Using High Speed Camera and 3D-Gradients Descriptor. In: The Proceedings of 3rd International Conference on Imaging for Crime Detection and Prevention, pp. 1–6 (2009)
Shreve, M., Godavarthy, S., Manohar, V., Goldgof, D., Sarkar, S.: Towards Macro- and Micro-Expression Spotting in Videos using Strain Patterns. In: The Proceeding of IEEE Workshop on Applications of Computer Vision, pp. 1–6 (2009)
Shreve, M., Godavarthy, S., Goldgof, D., Sarkar, S.: Macro- and Micro- Expression Spotting using Spatio-temporal Strain. To appear in Face and Gesture, Santa Barbara (March 2011), http://www.cse.usf.edu/~mshreve/publications/FG11.pdf
Kanade, T., Cohn, J.F., Tian, Y.: Comprehensive Database for Facial Expression Analysis. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pp. 46–53 (2000)
Kienzle, W., Bakir, G., Franz, B., Scholkopf, M.: Face Detection - Efficient and Rank Deficient. In: Advances in Neural Information Processing Systems, vol. 17, pp. 673–680 (2005)
Bartlett, M., Whitehill, J.: Automated Facial Expression Measurement: Recent Applications to Basic Research in Human Behavior, Learning, and Education. In: Calder, A., Rhodes, G., Haxby, J.V., Johnson, M.H. (eds.) Handbook of Face Perception. Oxford University Press, USA (2010), http://mplab.ucsd.edu/~marni/pubs/Bartlett_FaceHandbook_2010.pdf
Shen, L., Bai, L.: Mutualboost Learning for Selecting Gabor Features for Face Recognition. Pattern. Recogn. Lett. 27, 1758–1767 (2006)
Torralba, A., Murphy, K.P., Freeman, W.T.: Sharing Features: Efficient Boosting Procedures for Multiclass Object Detection. In: Proceedings of 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 762–769 (2004)
Whitehill, J., Littlewort, G., Fasel, I., Bartlett, M., Movellan, J.: Toward Practical Smile Detection. IEEE Trans. Pattern. Anal. Mach. Intell. 31, 2106–2111 (2009)
Gao, N., Tang, Q.: On Selection and Combination of Weak Learners in AdaBoost. Pattern Recogn. Lett. 31, 991–1001 (2010)
Jia, H., Zhang, Y.: Fast Adaboost Training Algorithm by Dynamic Weight Trimming. Chinese. J. Comput 32, 336–341 (2009)
Pantic, M., Valstar, M.F., Rademaker, R., Maat, L.: Web-Based Database for Facial Expression Analysis. In: Proceedings of IEEE International Conference on Multimedia and Expo., pp. 317–321 (2005)
Wallhoff, F.: Facial Expressions and Emotion Database. Technische Universität München (2006), http://www.mmk.ei.tum.de/~waf/fgnet/feedtum.html
Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding Facial Expressions with Gabor Wavelets. In: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 200–205 (1998)
Roy, S., Roy, C., Fortin, I., Either-Majcher, C., Belin, P., Gosselin, F.: A Dynamic Facial Expression Database. J. Vis. 7, 944a (2007)
Ekman, P., Friesen, W.V.: Pictures of Facial Affect. Consulting Psychologists Press, California (1976)
Russell, T.A., Elvina, C., Mary, L.P.: A Pilot Study to Investigate the Effectiveness of Emotion Recognition Remediation in Schizophrenia Using the Micro-Expression Training Tool. Brit. J. Clin. Psychol. 45, 579–583 (2006)
Koelstra, S., Pantic, M., Patras, I.: A Dynamic Texture-Based Approach to Recognition of Facial Actions and Their Temporal Models. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 1940–1954 (2010)
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Wu, Q., Shen, X., Fu, X. (2011). The Machine Knows What You Are Hiding: An Automatic Micro-expression Recognition System. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_16
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DOI: https://doi.org/10.1007/978-3-642-24571-8_16
Publisher Name: Springer, Berlin, Heidelberg
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