| 基于Faster R-CNN的人脸识别算法研究 |
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| 引用本文： | 景辉,阎志远,戴琳琳,李贝贝.基于Faster R-CNN的人脸识别算法研究[J].铁路计算机应用,2019,28(10):8-11. |
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| 作者姓名： | 景辉 阎志远 戴琳琳 李贝贝 |
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| 作者单位： | 中国铁道科学研究院集团有限公司 电子计算技术研究所, 北京 100081 |
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| 基金项目： | 中国铁道科学研究院研究开发计划项目(2017YJ096)中国铁道科学研究院电子所创新基金课题(DZYF17-01) |
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| 摘 要： | 人脸识别技术是身份认证的重要方式。旨在设计算法识别身份证人像与待检人像是否为同一旅客。使用卷积神经网络进行人脸识别算法的研究。使用检测人脸后计算人脸特征间欧式距离的方式进行算法设计,最终达到95%的正确率。结果表明, Faster R-CNN算法能较精准地检测人脸, VGG-Net可以较好地提取人脸特征值。
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| 关 键 词： | 卷积神经网络 人脸检测 人脸识别 Faster R-CNN算法 |
| 收稿时间： | 2018-11-21 |
Face recognition algorithm based on Faster R-CNN |
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| Institution: | Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China |
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| Abstract: | Face recognition technology is an important way of identity authentication. The purpose of this paper was to design an algorithm to identify whether the identity witness and the person to be inspected were the same passengers. Face recognition algorithm based on convolutional neural network was studied. The algorithm was designed by calculating the Euclidean distance between face features after face detection, and the final accuracy was 95%. The results show that Faster R-CNN algorithm can detect face accurately, VGG-Net can extract face feature values well. |
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