A Facial Expression Recognition Method Using Two-Stream Convolutional Networks in Natural Scenes
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초록
Aiming at the problem that complex external variables in natural scenes have a greater impact on facialexpression recognition results, a facial expression recognition method based on two-stream convolutionalneural network is proposed. The model introduces exponentially enhanced shared input weights before eachlevel of convolution input, and uses soft attention mechanism modules on the space-time features of thecombination of static and dynamic streams. This enables the network to autonomously find areas that are morerelevant to the expression category and pay more attention to these areas. Through these means, the informationof irrelevant interference areas is suppressed. In order to solve the problem of poor local robustness caused bylighting and expression changes, this paper also performs lighting preprocessing with the lighting preprocessingchain algorithm to eliminate most of the lighting effects. Experimental results on AFEW6.0 and Multi-PIEdatasets show that the recognition rates of this method are 95.05% and 61.40%, respectively, which are betterthan other comparison methods.
Abstract
Aiming at the problem that complex external variables in natural scenes have a greater impact on facialexpression recognition results, a facial expression recognition method based on two-stream convolutionalneural network is proposed. The model introduces exponentially enhanced shared input weights before eachlevel of convolution input, and uses soft attention mechanism modules on the space-time features of thecombination of static and dynamic streams. This enables the network to autonomously find areas that are morerelevant to the expression category and pay more attention to these areas. Through these means, the informationof irrelevant interference areas is suppressed. In order to solve the problem of poor local robustness caused bylighting and expression changes, this paper also performs lighting preprocessing with the lighting preprocessingchain algorithm to eliminate most of the lighting effects. Experimental results on AFEW6.0 and Multi-PIEdatasets show that the recognition rates of this method are 95.05% and 61.40%, respectively, which are betterthan other comparison methods.
참고문헌 (12)
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