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Proceedings Paper

Static facial expression recognition with convolution neural networks
Author(s): Feng Zhang; Zhong Chen; Chao Ouyang; Yifei Zhang
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Paper 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.

Paper Details

Date Published: 8 March 2018
PDF: 3 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 1060902 (8 March 2018); doi: 10.1117/12.2281998
Show Author Affiliations
Feng Zhang, Huazhong Univ of Science And Technology (China)
Zhong Chen, Huazhong Univ. of Science and Technology (China)
Chao Ouyang, Huazhong Univ. of Science and Technology (China)
Yifei Zhang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)

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