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 ResNet-18 _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. According to experimental analysis, our method achieves an accuracy of 98.28\% on the evaluation setof the Aff-Wild2 database . Compared with the traditional Bayesian Neural Network , our method brings the highest classification accuracy gain.
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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ResNet-18
ResNet18
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According to experimental analysis, our method achieves an accuracy of 98.28\% on the evaluation setof the Aff-Wild2 database
Through testing on the FER2013 test set, we achieved 71.5\% and 73.1\% accuracy in PublicTestSet and PrivateTestSet, respectively
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the traditional Bayesian Neural Network
traditional Bayesian neural networks