NDLI: Facial expression recognition based on transfer learning from deep convolutional networks

Content Provider	IEEE Xplore Digital Library
Author	Mao Xu Wei Cheng Qian Zhao Li Ma Fang Xu
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Univ. of Electron. Sci. & Technol. of China, Chengdu, China (Mao Xu; Wei Cheng; Qian Zhao; Li Ma) \|\| Sch. of Sci., Southwest Pet. Univ., Chengdu, China (Fang Xu)
Abstract	It is well-known that deep models could extract robust and abstract features. We propose a efficient facial expression recognition model based on transfer features from deep convolutional networks (ConvNets). We train the deep ConvNets through the task of 1580-class face identification on the MSRA-CFW database and transfer high-level features from the trained deep model to recognize expression. To train and test the facial expression recognition model on a large scope, we built a facial expression database of seven basic emotion states and 2062 imbalanced samples depending on four facial expression databases (CK+, JAFFE, KDEF, Pain expressions form PICS). Compared with 50.65% recognition rate based on Gabor features with the seven-class SVM and 78.84% recognition rate based on distance features with the seven-class SVM, we achieve average 80.49% recognition rate with the seven-class SVM classifier on the self-built facial expression database. Considering occluded face in reality, we test our model in the occluded condition and demonstrate the model could keep its ability of classification in the small occlusion case. To increase the ability further, we improve the facial expression recognition model. The modified model merges high-level features transferred from two trained deep ConvNets of the same structure and the different training sets. The modified model obviously improves its ability of classification in the occluded condition and achieves average 81.50% accuracy on the self-built facial expression database.
Starting Page	702
Ending Page	708
File Size	1128514
Page Count	7
File Format	PDF
ISSN	21579563
e-ISBN	9781467376792
DOI	10.1109/ICNC.2015.7378076
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Emotion recognition Facial expression recognition Databases Face recognition Transfer learning Feature extraction Robustness Deep convolutional networks Face
Content Type	Text
Resource Type	Article

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