Distinguishing Posed and Spontaneous Smiles by Facial Dynamics

Mandal, Bappaditya; Lee, David; Ouarti, Nizar

Abstract

Smile is one of the key elements in identifying emotions and present state of mind of an individual. In this work, we propose a cluster of approaches to classify posed and spontaneous smiles using deep convolutional neural network (CNN) face features, local phase quantization (LPQ), dense optical flow and histogram of gradient (HOG). Eulerian Video Magnification (EVM) is used for micro-expression smile amplification along with three normalization procedures for distinguishing posed and spontaneous smiles. Although the deep CNN face model is trained with large number of face images, HOG features outperforms this model for overall face smile classification task. Using EVM to amplify micro-expressions did not have a significant impact on classification accuracy, while the normalizing facial features improved classification accuracy. Unlike many manual or semi-automatic methodologies, our approach aims to automatically classify all smiles into either ‘spontaneous’ or ‘posed’ categories, by using support vector machines (SVM). Experimental results on large UvA-NEMO smile database show promising results as compared to other relevant methods.

Citation

Mandal, B., Lee, D., & Ouarti, N. (2017). Distinguishing Posed and Spontaneous Smiles by Facial Dynamics. In Computer Vision – ACCV 2016 Workshops (552-566). https://doi.org/10.1007/978-3-319-54407-6_37

• Conference Name: ACCV 2016 International Workshops
• Conference Location: Taipei, Taiwan
• Start Date: Nov 20, 2016
• End Date: Nov 24, 2016
• Online Publication Date: Mar 15, 2017
• Publication Date: Mar 14, 2017
• Deposit Date: Jun 14, 2023
• Publisher: Springer
• Pages: 552-566
• Series Title: Lecture Notes in Computer Science (LNCS, volume 10116)
• Book Title: Computer Vision – ACCV 2016 Workshops
• ISBN: 9783319544069
• DOI: https://doi.org/10.1007/978-3-319-54407-6_37