基于CNN的面部表情识别算法研究

Jian-liang Meng, Li-Guo Zheng
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引用次数: 3

摘要

为了快速准确地实现面部表情的识别,本文对传统的卷积神经网络进行了改进,提出了基于LeNet-5卷积神经网络算法的面部表情识别研究。首先对图像进行预处理,包括归一化和几何变换。其次,设计了基于LeNet-5模型的卷积神经网络模型,在模型中加入Flatten层,引入Dropout策略,以LeakReLU作为激活函数;最后,实验结果表明,改进算法的识别率优于LBP、sift和hog,但耗时最长,计算量最大。
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Research on Facial Expression Recognition Algorithm Based on CNN
In order to realize the recognition of facial expression quickly and accurately, the traditional convolutional neural network is improved in this paper, and the research of facial expression recognition based on LeNet-5 convolutional neural network algorithm is proposed. Firstly, the image is preprocessed, including normalization and geometric transformation. Secondly, the convolution neural network model based on LeNet-5 model is designed, in which the Flatten layer is added, the Dropout strategy is introduced, and the LeakReLU is used as the activation function. At last, the experimental results show that the recognition rate of the improved algorithm is better than that of LBP, sift and hog, but it takes the longest time and the largest amount of computation.
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