基于改进的SqueezeNet的人脸识别
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江西理工大学

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中图分类号:

TP391.41

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Face Recognition Based on Improved SqueezeNet
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Affiliation:

Jiangxi University of Science and Technology

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The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    传统的机器学习人脸识别方法容易受到角度,光照等条件影响,在卷积神经网络提出后得到改善。针对目前卷积神经网络在提高识别率上,忽视模型参数,导致参数量太大而无法在内存小的硬件上运行,提出一种基于SqueezeNet的改进模型识别人脸。SqueezeNet模型采用小的卷积核对特征提取,有利于降低模型参数输入。但是小的卷积核在复杂训练集上容易产生过拟合。为了防止过拟合,改进的SqueezeNet模型把首个池化层和最后个池化层分别引入到下一层的卷积层融合,可以提取更多特征信息,优化了特征信息丢失的情况。针对分类函数Softmax的改进,采用L2范数约束的方法,将最后一层的特征约束在一个球面内,易于收敛,防止过拟合。实验在CASIA-webface和ORL人脸库上验证了有效性。

    Abstract:

    The traditional machine learning face recognition method is easily affected by conditions such as angle and illumination, and is improved after the convolutional neural network is proposed. In view of the current convolutional neural network to improve the recognition rate, the model parameters are neglected, resulting in too large a parameter size to run on hardware with small memory. An improved model based on SqueezeNet is proposed to identify the face. The SqueezeNet model uses small convolution check kernel feature extraction, which is beneficial to reduce the model parameter input. However, small convolution kernels tend to over-fitting on complex training sets. In order to prevent over-fitting, the improved SqueezeNet model introduces the first pooling layer and the last pooling layer into the next layer of convolutional layer fusion, which can extract more feature information and optimize the loss of feature information. For the improvement of the classification function Softmax, the L2 norm constraint method is used to constrain the features of the last layer into a spherical surface, which is easy to converge and prevent over-fitting. The experiment validated the validity on the CASIA-webface and ORL face databases.

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引用本文

吴 军,邱 阳,卢忠亮. 基于改进的SqueezeNet的人脸识别[J]. 科学技术与工程, 2019, 19(11): .
Wu Jun, Qiu Yang, Lu Zhongliang. Face Recognition Based on Improved SqueezeNet[J]. Science Technology and Engineering,2019,19(11).

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历史
  • 收稿日期:2018-12-07
  • 最后修改日期:2019-02-20
  • 录用日期:2019-02-12
  • 在线发布日期: 2019-04-25
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