深度残差卷积下多视角特征融合的人脸表情识别

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本站小编 Free考研考试/2024-10-07

作者:\n\t关小蕊，高璐，宋文博，林克正\n

Authors:\n\tGUAN Xiaorui,GAO Lu,SONG Wenbo,LIN Kezheng\n
摘要:\n\t针对现实生活中多视角下人脸表情识别不够精准、计算量大等问题，提出了一种深度残差卷积下多视角特征融合的人脸表情识别模型MVResNet-FER。首先改进ResNet中的残差块，并使用深度可分离网络取代常规卷积网络。其次添加了CBAM模块，以增强多视角下有效特征的提取和浅层特征信息的补充。然后使用RReLu激活函数取代原始的ReLu，避免梯度较大时部分节点出现失活。最后使用全局平均池化层代替全连接层实现降维，并将生成的特征向量送入Softmax进行分类。实验表明，本文方法在CK+和RaFD数据集上产生了较优异的结果，能有效提高人脸表情识别的准确率。\n

Abstract:\n\tAiming at the problems of inaccurate facial expression recognition and large amount of calculation under multi-perspective in real life, a facial expression recognition model MVResNet-FER is proposed, which is based on multi-perspective feature fusion under deep residual convolution.The residual block in ResNet is first improved and the conventional convolutional network is replaced with a depthwise separable network.Second, a CBAM module is added to enhance the extraction of effective features under multi-perspective and the supplementation of shallow feature information.Then use the RReLu activation function to replace the original ReLu to avoid deactivation of some nodes when the gradient is large.Finally, the global average pooling layer is used instead of the fully connected layer to achieve dimensionality reduction, and the generated feature vector is sent to Softmax for classification Experiments show that the proposed method produces excellent results on the CK+ and RaFD datasets, which can effectively improve the accuracy of facial expression recognition.\n

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深度残差卷积下多视角特征融合的人脸表情识别

本站小编 Free考研考试/2024-10-07

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