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Cognitive Computation
Erschienen in: Cognitive Computation 5/2017

10.05.2017

Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition

verfasst von: Guihua Wen, Zhi Hou, Huihui Li, Danyang Li, Lijun Jiang, Eryang Xun

Erschienen in: Cognitive Computation | Ausgabe 5/2017

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Abstract

Convolutional neural network (CNN) is a very effective method to recognize facial emotions. However, the preprocessing and selection of parameters of these methods heavily depend on the human experience and require a large amount of trial-and-errors. This paper presents an ensemble of convolutional neural networks method with probability-based fusion for facial expression recognition, where the architecture of CNN was adapted by using the convolutional rectified linear layer as the first layer and multiple hidden maxout layers. It was constructed by randomly varying parameters and architecture around the optimal values for CNN, where each CNN as the base classifier was trained to output a probability for each class. These probabilities were then fused through the probability-based fusion method. The conducted experiments on benchmark data sets validated our method, which had better accuracy than the compared methods. The proposed method was novel and efficient for facial expression recognition.
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Metadaten
Titel
Ensemble of Deep Neural Networks with Probability-Based Fusion for Facial Expression Recognition
verfasst von
Guihua Wen
Zhi Hou
Huihui Li
Danyang Li
Lijun Jiang
Eryang Xun
Publikationsdatum
10.05.2017
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 5/2017
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-017-9472-6

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