Computer Engineering and Applications ›› 2019, Vol. 55 ›› Issue (5): 105-111.DOI: 10.3778/j.issn.1002-8331.1803-0419

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Research on New LeNet-FC Convolutional Neural Network Model Algorithm

BAI Chuang, CHEN Xiang   

  1. School of Physics and Electronic Science, Changsha University of Science and Technology, Changsha 410114, China
  • Online:2019-03-01 Published:2019-03-06


白  创,陈  翔   

  1. 长沙理工大学 物理与电子科学学院,长沙 410114

Abstract: Aiming at the problem of overfitting, slow convergence, local optimization and low recognition accuracy of existing Convolutional Neural Network(CNN) in face recognition training, a new LeNet-FC convolutional neural network model is proposed in this paper.By improving the network structure of the network layer, reducing the convolution kernel, and using the optimized Logarithmic Rectified Linear Unit(L_ReLU) activation function, the recognition accuracy of the model in face recognition training reaches 99.85%.Simultaneously, a face recognition system is designed based on the LeNet-FC convolutional neural network model. The recognition accuracy of the system in the simulation test experiment with ORL face database reaches 96%.

Key words: artificial intelligence, face recognition, convolutional neural network, network structure improvement, activation function optimization

摘要: 针对已有的卷积神经网络(Convolutional Neural Network,CNN)在人脸识别训练中出现过拟合、收敛速度慢以及识别准确率不高的问题,提出了新型的LeNet-FC卷积神经网络模型。通过增加网络层、缩小卷积核等结构改进以及采用优化的对数—修正线性单元(Logarithmic Rectified Linear Unit,L_ReLU)激活函数,该模型在人脸识别训练的准确率达到了99.85%。同时基于LeNet-FC卷积神经网络模型设计了一个人脸识别系统。该系统在ORL人脸库的仿真测试实验中识别准确率达到了96%。

关键词: 人工智能, 人脸识别, 卷积神经网络, 结构改进, 激活函数优化