Facial expression recognition based on image fusion and deep learning
Aiming at the problem that the texture feature extraction method is single and deep learning cannot effectively extract image local features,a facial expression recognition method based on image fusion and deep learning is proposed.Firstly,the local binary pattern(LBP)image and the Weber local descriptor(WLD)image are extracted from the facial expression image.Then,the two texture images are fused as the input image and sent to the improved residual neural network(ResNet)to extract facial expression features,the convolution kernel in ResNet is replaced with dilated convolution,and an improved attention mechanism is added to the network to make the model pay more attention to effective features.Finally,SoftMax is used for expression classification.Experiments are performed on the JAFFE and CK+datasets,and the accuracy rates are 97.0%and 99.3%,respectively.The experimental results show that this method can effectively improve the accuracy of facial expression recognition.
万方数据
维普
