Hierarchical committee of deep convolutional neural networks for robust facial expression recognition

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This paper describes our approach towards robust facial expression recognition (FER) for the third Emotion Recognition in the Wild (EmotiW2015) challenge. We train multiple deep convolutional neural networks (deep CNNs) as committee members and combine their decisions. To improve this committee of deep CNNs, we present two strategies: (1) in order to obtain diverse decisions from deep CNNs, we vary network architecture, input normalization, and random weight initialization in training these deep models, and (2) in order to form a better committee in structural and decisional aspects, we construct a hierarchical architecture of the committee with exponentially-weighted decision fusion. In solving a seven-class problem of static FER in the wild for the EmotiW2015, we achieve a test accuracy of 61.6 %. Moreover, on other public FER databases, our hierarchical committee of deep CNNs yields superior performance, outperforming or competing with state-of-the-art results for these databases.
Publisher
Springer
Issue Date
2016-06
Language
English
Article Type
Article
Citation

JOURNAL ON MULTIMODAL USER INTERFACES, v.10, no.2, pp.173 - 189

ISSN
1783-7677
DOI
10.1007/s12193-015-0209-0
URI
http://hdl.handle.net/10203/212286
Appears in Collection
EE-Journal Papers(저널논문)
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