基于Swish激活函数的人脸情绪识别的深度学习模型研究

A Research on Deep Learning Model for Face Emotion Recognition Based on Swish Activation Function

摘要: 近年来，深度学习模型得到飞速发展，深度卷积神经网络作为其中一种方法在计算机视觉中得到广泛应用。影响深度学习模型性能的因素众多，其中激活函数的选取和神经网络的结构对深度学习模型的性能有着重要的影响。本文分析了传统激活函数与新型Swish激活函数的优缺点，将Swish函数引入人脸情绪深度学习模型，提出了一种改进的反向传播算法，并在卷积神经网络中使用多层小尺寸卷积模块替代大尺寸卷积模块，提取细化特征，构建了一种新型的人脸情绪识别深度学习模型Swish-FER-CNNs。实验结果表明，相比于ReLU、L-ReLU、P-ReLU等激活函数，基于Swish激活函数的深度学习模型的识别准确率更高。结合改进的网络结构，本文构建的深度学习模型Swish-FER-CNNS相对于现存模型，识别准确率提高了4.02%。

Abstract: In recent years, deep learning model has been developed rapidly. As one of the methods, deep convolution neural network has been widely used in computer vision. There are many factors af-fecting the performance of deep learning model, among which the selection of activation function and the structure of neural network have important impact on the performance of deep learning model. This paper analyses the advantages and disadvantages of the traditional activation function and the new Swish activation function, introduces Swish function into the deep learning model of facial emotion, proposes an improved back propagation algorithm, and uses multi-layer small-size convolution module instead of large-size convolution module in the convolution neural network to extract refinement features, and constructs a new deep learning model of facial emotion recognition, Swish-FER-CNNs. The experimental results show that the recognition accuracy of deep learning model based on Swish activation function is higher than that of activation functions such as ReLU, L-ReLU and P-ReLU. With the improved network structure, the recognition accuracy of the deep learning model of Swish-FER-CNNS constructed in the paper is improved by 4.02% compared with the existing model.