Expression recognition method based on double-branch mixed residual connection
The invention provides an expression recognition method based on double-branch mixed residual connection. The method specifically comprises the steps that ResNet18 is used as a trunk feature extraction network, a double-branch multi-residual connection mode is designed on the basis, and feature extr...
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| creator | LU XIUWEN ZHANG QI HAN XUE ZHANG HONGYING |
| description | The invention provides an expression recognition method based on double-branch mixed residual connection. The method specifically comprises the steps that ResNet18 is used as a trunk feature extraction network, a double-branch multi-residual connection mode is designed on the basis, and feature extraction is completed; secondly, comprehensively analyzing problems in two aspects of network depth and width, fusing depth separable convolution, and constructing a lightweight expression recognition network; and finally, according to the characteristics of the facial expression information, in order to obtain more accurate and perfect feature information, adding an adaptive feature fusion module ASFF, carrying out analysis and verification on the fused information, selecting a hierarchy with the most superior expression for output, and carrying out final expression classification prediction. According to the method, a perfect feature extraction mechanism and a self-adaptive feature fusion mode are combined, and com |
| format | Patent |
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The method specifically comprises the steps that ResNet18 is used as a trunk feature extraction network, a double-branch multi-residual connection mode is designed on the basis, and feature extraction is completed; secondly, comprehensively analyzing problems in two aspects of network depth and width, fusing depth separable convolution, and constructing a lightweight expression recognition network; and finally, according to the characteristics of the facial expression information, in order to obtain more accurate and perfect feature information, adding an adaptive feature fusion module ASFF, carrying out analysis and verification on the fused information, selecting a hierarchy with the most superior expression for output, and carrying out final expression classification prediction. 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| subjects | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING HANDLING RECORD CARRIERS PHYSICS PRESENTATION OF DATA RECOGNITION OF DATA RECORD CARRIERS |
| title | Expression recognition method based on double-branch mixed residual connection |
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