1 Affiliation : Bharati Vidyapeeth's College of Engineering, INDIA
Email : dubeyvishal1998@gmail
2 Affiliation : Bharati Vidyapeeth's College of Engineering, INDIA
Email : email@example.com
3 Affiliation : Bharati Vidyapeeth's College of Engineering, INDIA
Email : firstname.lastname@example.org
Micro-expression comes under nonverbal communication, and for a matter of fact, it appears for minute fractions of a second. One cannot control micro-expression as it tells about our actual state emotionally, even if we try to hide or conceal our genuine emotions. As we know that micro-expressions are very rapid due to which it becomes challenging for any human being to detect it with bare eyes. This subtle-expression is spontaneous, and involuntary gives the emotional response. It happens when a person wants to conceal the specific emotion, but the brain is reacting appropriately to what that person is feeling then. Due to which the person displays their true feelings very briefly and later tries to make a false emotional response. Human emotions tend to last about 0.5 - 4.0 seconds, whereas micro-expression can last less than 1/2 of a second. On comparing micro-expression with regular facial expressions, it is found that for micro-expression, it is complicated to hide responses of a particular situation. Micro-expressions cannot be controlled because of the short time interval, but with a high-speed camera, we can capture one's expressions and replay them at a slow speed. Over the last ten years, researchers from all over the globe are researching automatic micro-expression recognition in the fields of computer science, security, psychology, and many more. The objective of this paper is to provide insight regarding micro-expression analysis using 3D CNN. A lot of datasets of micro-expression have been released in the last decade, we have performed this experiment on SMIC micro-expression dataset and compared the results after applying two different activation functions.
3D CNN; micro-expression; Micro-Expression Recognition
 P. Ekman, "An argument for basic emotions," Cognition and Emotion, vol. 6, pp. 169–200, 1992.
 Paul Ekman, Emotions Revealed: Understanding Faces and Feelings. Phoenix, 2004.
 P. Ekman and E. L. Rosenberg, What the Face Reveals: Basic and Applied Studies of Spontaneous Expression Using the Facial Action Coding System (FACS), ser. Series in Affective Science. Oxford University Press, 2005.
 J. A. Russell and J. M. Ferna'ndez-Dols, The psychology of facial expression. Cambridge university press, 1997.
 P. Ekman, "Lie catching and microexpressions," in The Philosophy of Deception, C. W. Martin, Ed. Oxford University Press, 2009, pp. 118–133.
 D. Matsumoto, S. H. Yoo, and S. Nakagawa, "Culture, emotion regulation, and adjustment." Journal of personality and social psychology, vol. 94, no. 6, p. 925, 2008.
 M. O'Sullivan, M. G. Frank, C. M. Hurley, and J. Tiwana, "Police lie detection accuracy: The effect of lie scenario." Law and Human Behavior, vol. 33, no. 6, p. 530, 2009.
 M. G. Frank, C. J. Maccario, and V. l. Govindaraju, "Behavior and security," in Protecting airline passengers in the age of terrorism. Greenwood Pub. Group, 2009.
 M. Frank, M. Herbasz, K. Sinuk, A. M. Keller, A. Kurylo, and C. Nolan, "I see how you feel: Training lay people and professionals to recognize fleeting emotions," in International Communication Association, 2009.
 W.-J. Yan, Q. Wu, J. Liang, Y.-H. Chen, and X. Fu, "How fast are the leaked facial expressions: The duration of micro-expressions," Journal of Nonverbal Behavior, vol. 37, no. 4, pp. 217–230, 2013.
 P. Ekman, Telling Lies: Clues to Deceit in the Marketplace, Politics, and Marriage. Norton, 2001.
 W. Merghani, A. K. Davison, and M. H. Yap, "A review on facial microexpressions analysis: Datasets, features and metrics," arXiv preprint arXiv:1805.02397, 2018.
 J. Li , Y.Wang,J.See,W.Liu , "Micro-expression recognition based on 3D flow convolutional neural network" in Springer-Verlag London,2018.
 He K, Zhang X, Ren S, Sun J," Deep residual learning for image recognition." arXiv preprint arXiv :1512.03385 , 2015.
 Zeiler MD, Fergus , "Visualizing and understanding convolutional networks." In Comper vision–ECCV 2014, Springer, pp 818–833
 S P.T. Reddy, S T.Karri, S R. Dubey, S. Mukherjee," Spontaneous Facial Micro-Expression Recognition using 3D spatiotemporal Convolutional neural networks",2019
 S. Polikovsky, Y. Kameda, and Y. Ohta, "Facial micro-expressions recognition using high speed camera and 3d-gradient descriptor," in Crime Detection and Prevention (ICDP 2009), 3rd International Conference on. IET, 2009, pp. 1–6.
 M.Shreve,S.Godavarthy, D.Goldgof,andS.Sarkar, "Macro-and micro-expression spotting in long videos using spatio-temporal strain," in 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011), 2011, pp. 51–56.
 G. Warren, E. Schertler, and P. Bull, "Detecting deception from emotional and unemotional cues," Journal of Nonverbal Behavior, vol. 33, no. 1, pp. 59–69, 2009.
 W.-J. Yan, Q. Wu, Y.-J. Liu, S.-J. Wang, and X. Fu, "Casme database: a dataset of spontaneous micro-expressions collected from neutralized faces," in Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops on. IEEE, 2013, pp. 1–7.
 X. Li, T. Pﬁster, X. Huang, G. Zhao, and M. Pietikainen, "A spontaneous micro-expression database: Inducement, collection and baseline," in Automatic Face and Gesture Recognition(FG),2013 10th IEEE International Conference and Workshops on. IEEE, 2013, pp. 1–6.
 W.-J. Yan, X. Li, S.-J. Wang, G. Zhao, Y.-J. Liu, Y.-H. Chen, and X. Fu, "Casme ii: An improved spontaneous micro-expression database and the baseline evaluation," PloS one, vol. 9, no. 1, 2014.
 A. K. Davison, C. Lansley, N. Costen, K. Tan, and M. H. Yap, "Samm: A spontaneous micro-facial movement dataset," IEEE Transactions on Affective Computing, vol. 9, no. 1, pp. 116–129, Jan 2018.
 F. Qu, S.-J. Wang, W.-J. Yan, H. Li, S. Wu, and X. Fu, "Cas (me)ˆ2: A database for spontaneous macro-expression and micro-expression spotting and recognition," IEEE Transactions on Affective Computing, 2017.
 G. Zhao and M. Pietikainen, "Dynamic texture recognition using local binary patterns with an application to facial expressions," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 29, pp. 915-928, June 2007.