Static facial expression recognition with convolution neural networks

Feng Zhang; Zhong Chen; Chao Ouyang; Yifei Zhang

doi:10.1117/12.2281998

8 March 2018 Static facial expression recognition with convolution neural networks

Feng Zhang; Zhong Chen; Chao Ouyang; Yifei Zhang

Author Affiliations +

Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 1060902 (2018) https://doi.org/10.1117/12.2281998
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China

Abstract

Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.

Citation Download Citation

Feng Zhang, Feng Zhang, Zhong Chen, Zhong Chen, Chao Ouyang, Chao Ouyang, Yifei Zhang, Yifei Zhang, } "Static facial expression recognition with convolution neural networks", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060902 (8 March 2018); doi: 10.1117/12.2281998; https://doi.org/10.1117/12.2281998

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