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arXiv · 2107.04834

Bayesian Convolutional Neural Networks for Seven Basic Facial Expression Classifications

Yuan Tai·Yihua Tan·Wei Gong·Hailan Huang
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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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Yuan Tai, Yihua Tan, Wei Gong, Hailan Huang. 2021-07-13. Bayesian Convolutional Neural Networks for Seven Basic Facial Expression Classifications. https://arxiv.org/abs/2107.04834

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