[1]胡 强,张太红,赵昀杰,等.基于多模型联合的身份证人脸验证应用研究[J].计算机技术与发展,2021,31(08):209-214.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 036]
　HU Qiang,ZHANG Tai-hong,ZHAO Yun-jie,et al.Research on Application of ID Face Authentication Based onMulti-model Union[J].,2021,31(08):209-214.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 036]
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基于多模型联合的身份证人脸验证应用研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年08期
页码:
209-214
栏目:
应用前沿与综合
出版日期:
2021-08-10

文章信息/Info

Title:
Research on Application of ID Face Authentication Based onMulti-model Union
文章编号:
1673-629X(2021)08-0209-06
作者:
胡 强; 张太红; 赵昀杰; 迪力夏提·多力昆
新疆农业大学 计算机与信息工程学院,新疆 乌鲁木齐 830000
Author(s):
HU Qiang; ZHANG Tai-hong; ZHAO Yun-jie; Dilixiati DUOLIKUN
School of Computer and Information Engineering,Xinjiang Agricultural University,Urumqi 830000,China
关键词:
目标检测; 人脸验证; Mask R-CNN; 身份证; 多模型联合
Keywords:
object detection; face verification; Mask R-CNN; ID card; multi-model association
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 08. 036
摘要:
随着互联网技术的发展与普及,各应用软件以“井喷冶之势进入人们的生活,而网络实名认证制度伴随着电子商务的发展也出现在大众视野中,在传统实名认证模块中,由于用户上传证件照片不完整、模糊和类别错误等问题,导致人脸验证准确率下降。 该文通过对公民身份证的特点进行分析,基于 Mask R-CNN 算法对身份证及其关键信息进行像素级别的目标检测,对用户上传的证件进行质量判别,并根据判别结果联合 MTCNN 模型与 FaceNet 模型完成人脸的验证。 最终在自建测试数据集上进行对比实验。 实验结果表明,相比于原系统,使用多模型联合的身份证人脸验证方法,在不影响人脸识别精度的前提下,保证了上传证件的有效性和安全性,其中证件的查全率达到了 99. 24% ,整个系统的准确率为95. 20% 。
Abstract:
With the development and popularization of Internet technology,various application software has entered people’s life with the trend of "blowout" . With the development of e-commerce,the network real-name authentication system also appears in the public view. In the traditional real-name authentication module, the accuracy of face verification is reduced due to the incomplete,fuzzy and wrong category of photos uploaded by users. Based on the analysis of the characteristics of citizen ID card, we use Mask R-CNN algorithm to detect the identity card and its key information on pixel level, and judge the quality of the user爷 s uploaded ID card.According to the result of the discrimination,the MTCNN model and FaceNet model are combined to complete the face verification.Finally,a comparative experiment was carried out on the self built test data set. The experiment shows that compared with the originalsystem,the ID card face verification method based on multi-model combination ensures the validity and security of the uploaded ID card without affecting the accuracy of face recognition. The recall rate of the ID card reaches 99. 24% ,and the accuracy rate of the whole system is 95. 20% .

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更新日期/Last Update: 2021-08-10