An Automatic Multilevel Facial Expression Recognition System

Yıl 2018, Cilt: 22 Sayı: 1, 160 - 165, 16.03.2018

Elena Battını Sönmez

https://doi.org/10.19113/sdufbed.50007

Cited By: 3

Öz

Facial expression is one of the most natural way of human beings to communicate his-her internal feeling, to stress his-her words, to agree or disagree with the interlocutor, to regulate interaction with the environment and nearby people. This paper challenges the classification experiment run by human beings on the ADFES-BIV database, which is a recently introduced collection of videos expressing low, middle, and high intensity emotions. The proposed automatic system uses the Sparse Representation based Classifier and reaches the top performance of 80 % by considering the temporal information intrinsically present in the videos.

Anahtar Kelimeler

Expression recognition, Affective computing; Sparse representation based classifier

Kaynakça

• [1] Darwin, C. 1872. The Expression of the Emotions in Man and Animals. London, England: John Murray; 374 p.
• [2] Ambadar, Z., Schooler, J.W., Cohn, J.F. 2005. Deciphering the Enigmatic Face. Psychological Science, 16(2005), 403–410.
• [3] Marsh, A.A., Kozak, M.N., Ambady, N. 2007. Accurate Identification of Fear Facial Expressions Predicts Prosocial Behavior. Emotion, 7(2007), 239–251.
• [4] Scherer, K.R., Mortillaro, M., Mehu, M. 2013. Understanding the Mechanisms Underlying the Production of Facial Expression of Emotion: A Componential Perspective. Emotion Review 5(2013), 47–53.
• [5] Lander, K., Butcher, N. 2015. Independence of Face Identity and Expression Processing: Exploring the Role of Motion. Frontiers in Psychology. 1(2015), 6-255.
• [6] Wehrle, T., Kaiser, S., Schmidt, S., Scherer, K.R. 2000. Studying the Dynamics of Emotion Expression Using Synthesized Facial Muscle Movements. Journal of Personality and Social Psychology, 78(2000), 105-119.
• [7] Wingenbach, T.S.H., Ashwin, C., Brosnan, M. 2016. Validation of the Amsterdam Dynamic Facial Expression Set – Bath Intensity Variations (ADFES-BIV): A Set of Videos Expressing Low, Intermediate, and High Intensity Emotions. PLoS ONE, 11(2016), e0147112.
• [8] Ekman, P. 1992. An Argument for Basic Emotions. Cognition and Emotion. 6(1992), 169–200.
• [9] Kanade, T., Cohn, J.F., Tian, Y. 2000. Comprehensive Database for Facial Expression Analysis. 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG), 28-30 March, Grenoble, France, 46–53.
• [10] Lucey, P., Cohn, J.F., Kanade, T., Saragih, J., Ambadar, Z., Matthews, I. 2010. The Extended Cohn-Kanade Dataset (CK+): A Complete Dataset for Action Unit and Emotion-Specified Expression. IEEE workshop on CVPR for Human Communicative Behavior Analysis, 13-18 June, San Francisco, CA, USA. DOI: 10.1109/CVPRW.2010.5543262.
• [11] Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J. 1998. Coding Facial Expressions with Gabor Wavelets. IEEE Int. Conf. on Automatic Face and Gesture Recognition, 14-16 April, Nara, Japan, 200–205.
• [12] Pantic, M., Valstar, M., Rademaker, R., Maat, L. 2005. Web-Based Database for Facial Expression Analysis. IEEE Int. Conf. on Multimedia and Expo, 6 July, Amsterdam, Netherlands.
• [13] Dhall, A. Goecke, R., Joshi, J., Hoey, J., Gedeon, T. 2016. EmotiW 2016: Video and Group-Level Emotion Recognition Challenges. ACM ICMI, 12-16 November, Tokyo, Japan.
• [14] Bould, E., Morris, N. 2008. Role of Motion Signals in Recognizing Subtle Facial Expressions of Emotion. British Journal of Psychology, 99(2008), 167–189.
• [15] Yang, P., Liu, Q., Metaxas, D.N. 2010. Exploring Facial Expressions with Compositional Features. IEEE Int. Conf. on Computer Vision and Pattern Recognition (CVPR), 13-18 June, San Francisco, CA, USA.
• [16] Wu, T., Barlett, M.S., Movellan, J.R. 2010. Facial Expression Recognition Using Gabor Motion Energy Filters. IEEE Computer Society Conf. on Computer Vision and Pattern Recognition Workshops (CVPRW), 13-18 June San Francisco, CA, USA.
• [17] Jia, Q. Liu, Y. Guo, H., Luo, Z., Wang, Y. 2011. A Sparse Representation Approach for Local Feature Based Expression Recognition. Int. Conf. Multimedia Technology (ICMT), 26-28 July, Hangzhou, China.
• [18] Jeni, L.A., Girard, J.M., Cohn, J.F., De la Torre, F. 2013. Continuous AU Intensity Estimation Using Localized, Sparse Facial Feature Space. 10th IEEE Int. Conf. and Workshops on Automatic Face and Gesture Recognition (FG), 22-26 April, Shanghai, China.
• [19] Surace, L., Patacchiola, M., Battini Sönmez, E., Spataro, W., Cangelosi, A. 2017. Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers. 19th ACM Int. Conf. on Multimodal Interaction (ICMI’17), November 13–17, Glasgow, UK.
• [20] Van der Schalk, J., Hawk, S.T., Fischer, A.H., Doosje, B. 2011. Moving Faces, Looking Places: Validation of the Amsterdam Dynamic Facial Expression Set (ADFES). Emotion, 11(2011), 907–920.
• [21] Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y. 2009. Robust Face Recognition via Sparse Representation. Transactions on Pattern Analysis and Machine Intelligence, 31(2):210–227.
• [22] Battini Sönmez, E. 2013. Robust Classification Based on Sparsity. Lambert Academic Publishing, Germany, 99p, ISBN: 978-3-659-40066-7.
• [23] Battini Sönmez, E., Albayrak, S. 2013. A Study on the Critical Parameters of the Sparse Representation based Classifier. IET Computer Vision Journal, 7(2013), 500-507.