基于Faster R-CNN的人脸识别算法研究

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

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

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)

详细信息

作者简介:
景辉,研究实习员;阎志远,副研究员。

中图分类号: U285.49;TP391
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出版历程
- 收稿日期: 2018-11-20

Face recognition algorithm based on Faster R-CNN

Institute of Computing Technologies, China Academy of Railway Sciences Corporation Limited, Beijing 100081, China

摘要

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。
- 卷积神经网络 /
- 人脸检测 /
- 人脸识别 /
- Faster R-CNN算法
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.
- convolutional neural network /
- face detection /
- face recognition /
- Faster R-CNN algorithm

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参考文献(14)

[1]	Yang G, Huang T S. Human face detection in a complex background[J]. Pattern Recognition, 1994, 27(1):53-63.
[2]	Kotropoulos C, Pitas I. Rule-based face detection in frontal views[C]//IEEE International Conference on Acoustics, Speech, and Signal Processing, 2002:2537-2540.
[3]	李华胜,杨桦,袁保宗. 人脸识别系统中的特征提取[J]. 北京交通大学学报, 2001, 25(2):18-21.
[4]	姜军,张桂林. 一种基于知识的快速人脸检测方法[J]. 中国图象图形学报, 2002, 7(1):6-10.
[5]	卢春雨,张长水. 基于区域特征的快速人脸检测法[J]. 清华大学学报(自然科学版), 1999(1):101-105.
[6]	Graf H P., Chen T., Petajan E., et al. Locating faces and facial parts[C]//Proc. of International Workshop on Automatic Faceand Gesture-Recognition, 1995:41-46.
[7]	Leung T K, Burl M C, Perona P. Finding Faces in Cluttered Scenes using Random Labeled Graph Matching[C]//International Conference on Computer Vision, 1995:637.
[8]	Kirby M, Sirovich L. Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002,12(1):103-108.
[9]	Turk M, Pentland A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1):71-86.
[10]	Ren S, He K, Girshick R, et al. Faster R-CNN:Towards RealTime Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6):1137-1149.
[11]	李鹍,吴宁,宋明,等. 基于级联滤波器深度学习的铁路安检人脸识别与验证研究[J]. 铁路计算机应用, 2018(6):17:20.
[12]	He K., Zhang X., Ren S., et al. Deep Residual Learning for Image Recognition[C]//IEEE Conference on Computer Vision & Pattern Recognition, 2016.
[13]	Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. Computer Science, 2014:1-14.
[14]	贾成强,戴琳琳,徐海涛,等. 基于人脸识别技术的铁路实名制进站核验系统研究及设计[J]. 铁路计算机应用, 2018(7):49-54.

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