Bayesian Convolutional Neural Networks for Seven Basic Facial Expression Classifications

• URL: http://arxiv.org/abs/2107.04834v2
• Date: Tue, 13 Jul 2021 13:05:36 GMT
• Title: Bayesian Convolutional Neural Networks for Seven Basic Facial Expression Classifications
• Authors: Yuan Tai, Yihua Tan, Wei Gong, Hailan Huang
• Abstract summary: Seven basic facial expression classifications are a basic way to express complex human emotions. Based on the traditional Bayesian neural network framework, the ResNet18_BNN network constructed in this paper has been improved.
• Score: 5.365808418695478
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The seven basic facial expression classifications are a basic way to express complex human emotions and are an important part of artificial intelligence research. Based on the traditional Bayesian neural network framework, the ResNet18_BNN network constructed in this paper has been improved in the following three aspects: (1) A new objective function is proposed, which is composed of the KL loss of uncertain parameters and the intersection of specific parameters. Entropy loss composition. (2) Aiming at a special objective function, a training scheme for alternately updating these two parameters is proposed. (3) Only model the parameters of the last convolution group. Through testing on the FER2013 test set, we achieved 71.5% and 73.1% accuracy in PublicTestSet and PrivateTestSet, respectively. Compared with traditional Bayesian neural networks, our method brings the highest classification accuracy gain.

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