Abstract

Facial expressions are one of the most important behavioral measures for emotion recognition. Expressions can tell a lot about the person, his behavior, what he is thinking and this data is vital in making various predictions which can have a variety of applications. In this paper we have implemented and compared three types of facial expression recognition and classification techniques. The first one is a state-of-the-art convolutional neural network, the second one is a transfer learning approach using the InceptionV3 model and in the last one, we have extracted the 68 facial points which have been identified as important for recognizing the expression of a person and passed it to a deep neural network. All these techniques have given accuracies over 90%, so comes the need to compare them in detail and determine which one of them would give results more accurately and efficiently.

References

• Lucey, Patrick, et al. "The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression." Computer Vision and Pattern Recognition Workshops (CVPRW), 2010.
• Ekman, Paul, and Wallace V. Friesen. "Constants across cultures in the face and emotion." Journal of personality and social psychology 17.2 (1971): 124.
• Suwa, M., Sugie, N., Fujimora, K.: A preliminary note on pattern recognition of human emotional expression. In: International Joint Conference on Pattern Recognition, pp. 408– 410 (1978).
• Lecun, Y. "Generalization and Network Design Strategies." Connectionism in Perspective 1989.
• Lyons, Michael J., Julien Budynek, and Shigeru Akamatsu. "Automatic classification of single facial images." IEEE Transactions on pattern analysis and machine intelligence 21.12 (1999): 1357-1362.
• Kulkarni, Ketki R., and Sahebrao B. Bagal. "Facial expression recognition." India Conference (INDICON), 2015 Annual IEEE. IEEE, 2015.
• Ojala, Timo, Matti Pietikäinen, and David Harwood. "A comparative study of texture measures with classification based on featured distributions." Pattern recognition 29.1 (1996): 51-59.
• Shan, Caifeng, Shaogang Gong, and Peter W. McOwan. "Robust facial expression recognition using local binary patterns." Image Processing, 2005. ICIP 2005. IEEE International Conference on. Vol. 2. IEEE, 2005.
• Turk, Matthew A., and Alex P. Pentland. "Face recognition using eigenfaces." Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on. IEEE, 1991.
• Gosavi, Ajit P., and S. R. Khot. "Facial expression recognition using principal component analysis." International Journal of Soft Computing and Engineering (IJSCE) 3.4 (2013): 2231-2307.
• Bartlett, Marian Stewart, Javier R. Movellan, and Terrence J. Sejnowski. "Face recognition by independent component analysis." IEEE Transactions on neural networks 13.6 (2002): 1450-1464.
• Guo, XiaoHui, et al. "Facial Expression Recognition based on Independent Component Analysis." Journal of Multimedia 8.4 (2013).
• Belhumeur, Peter N., João P. Hespanha, and David J. Kriegman. "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection." IEEE Transactions on pattern analysis and machine intelligence 19.7 (1997): 711-720.
• Tong, Ying, Rui Chen, and Yong Cheng. "Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle." Optik-International Journal for Light and Electron Optics 125.16 (2014): 4186-4189.
• Jabid, Taskeed, Md Hasanul Kabir, and Oksam Chae. "Facial expression recognition using local directional pattern (LDP)." Image Processing (ICIP), 2010 17th IEEE International Conference on. IEEE, 2010.
• Szegedy, Christian, et al. "Rethinking the inception architecture for computer vision." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
• Deng, Jia, et al. "Imagenet: A large-scale hierarchical image database." Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.