基于改进卷积神经网络的面部表情识别

Jiancheng Zou, Xiuling Cao, Sai Zhang, Bailin Ge
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引用次数: 4

摘要

针对传统面部表情识别方法识别率低、算法复杂的问题,提出了一种基于卷积神经网络(CNN)的改进面部表情识别算法。卷积神经网络采用批处理正则化和ReLU激活函数来解决梯度消失问题。引入Dropout技术来解决网络过拟合问题。实验结果表明,改进的卷积神经网络可以提高人脸表情图像识别的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Facial Expression Recognition Based on Improved Convolutional Neural Network
In order to solve the problems of low recognition rate and complex algorithm of traditional facial expression recognition methods, an improved facial expression recognition algorithm based on convolutional neural network (CNN) was proposed. The convolutional neural network uses batch regularization and ReLU activation function to solve the problem of gradient disappearance. The Dropout technology is introduced to solve the problem of network overfitting. Experimental results show that the improved convolutional neural network can improve the accuracy of face expression image recognition.
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