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A Deep Face Recognition Method Based on Model Fine-tuning and Principal Component Analysis
Zhang YA(张延安)1,2; Wang, HY(王宏玉)1,3; Xu F(徐方)1,3; Jia K(贾凯)1,3
作者部门其他
会议名称7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
会议日期July 31 - August 4, 2017
会议地点Hawaii, USA
会议主办者IEEE Robotics and Automation Society
会议录名称2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017
出版者IEEE
出版地New York
2017
页码141-146
收录类别EI ; CPCI(ISTP)
EI收录号20183905873612
WOS记录号WOS:000447628700027
产权排序1
ISBN号978-1-5386-0489-2
关键词Face Recognition Deep Learning Model Fine-tuning Principal Component Analysis Convolutional Neural Networks
摘要

In this paper, we propose a simple and effective deep face recognition method based on model fine-tuning and principal component analysis. At first, we use our own face dataset to fine tune the improved VGG-Face model. This can effectively solve the problem that the training dataset is too small and the data distribution is different. Through the part of the existing model parameters as the initial parameters of the new model, greatly accelerated the convergence rate of the model training. Then, for the facial features extracted by the deep learning method, we use principal component analysis to further remove redundant features, reduce the complexity of the features, and improve the face recognition rate. The experimental results prove that the proposed approach achieves a good face recognition accuracy on our test dataset.

语种英语
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文献类型会议论文
条目标识符http://ir.sia.cn/handle/173321/22821
专题其他
通讯作者Zhang YA(张延安)
作者单位1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.University of Chinese Academy of Sciences, Beijing 100049, China
3.Shenyang SIASUN Robot & Automation Co., LTD., China, Shenyang 110168,China
推荐引用方式
GB/T 7714
Zhang YA,Wang, HY,Xu F,et al. A Deep Face Recognition Method Based on Model Fine-tuning and Principal Component Analysis[C]//IEEE Robotics and Automation Society. New York:IEEE,2017:141-146.
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