系统仿真学报 ›› 2015, Vol. 27 ›› Issue (10): 2291-2298.

• 人工智能与仿真 • 上一篇    下一篇

基于稀疏表示的云环境中人脸图像隐秘识别方法

刘妍1,2, 金鑫1, 赵耿1, 李晓东1, 陈迎亚1, 郭魁1   

  1. 1.北京电子科技学院,北京 100070;
    2.西安电子科技大学,西安 710071
  • 收稿日期:2015-06-13 修回日期:2015-07-23 出版日期:2015-10-08 发布日期:2020-08-07
  • 通讯作者: 金鑫(1983-),男,安徽安庆,博士,讲师,研究方向为可视计算与安全。
  • 作者简介:刘妍(1990-),女,陕西西安,硕士生,研究方向为人脸识别,计算机信息安全与保密。
  • 基金资助:
    国家自然科学基金(61170037, 61402021); 中央高校基本科研业务费资助(2014XSYJ01)

Sparse Representation Based Private Face Recognition in the Cloud

Liu Yan1,2, Jin Xin1, Zhao Geng1, Li Xiaodong1, Chen Yingya1, Guo Kui1   

  1. 1.Beijing Electronic Science and Technology Institute, Beijing 100070, China;
    2.Xidian University, Xi'an 710071, China
  • Received:2015-06-13 Revised:2015-07-23 Online:2015-10-08 Published:2020-08-07

摘要: 为了在保护用户隐私的前提下,完成用户人脸图像数据在云端的识别计算,提出了一种基于稀疏表示的云环境中人脸图像隐秘识别方法。终端采集的人脸图像与云端人脸数据库中的人脸图像进行比对,从而判断采集的人脸是否属于云端的人脸数据库,同时双方互相无法获取对方的人脸图像内容。该方法通过第三方人脸图像数据库对人脸图像分别进行稀疏表示,利用Paillier同态加密与不经意传输算法,隐秘的比对终端和云端人脸的稀疏系数向量,返回是否匹配。该方法将同态加密和不经意传输应用于稀疏表示人脸识别中,使得在云环境中实现保护隐私的人脸图像识别。

关键词: 隐秘计算, 人脸识别, 稀疏表示, 同态加密, 不经意传输

Abstract: In order to protect user privacy, identification computing of user facial image data in the cloud was accomplished, the sparse representation based private face recognition method in the cloud was proposed. Terminal users collected face images and compared with face images in cloud database and then determined whether faces the terminal acquired belonged to the cloud face database, but both could not achieve each other's face image content. The method processed face images in database and cloud into sparse representation via a third-party terminal, then using Paillier and oblivious transfer homomorphic encryption algorithm to contrast sparse representation coefficient vector of faces in terminal and cloud secretly, returning message that match or not. The innovation of this method is to apply homomorphic encryption and oblivious transfer into sparse representation face recognition, so that the private face recognition in the cloud can be achieved.

Key words: private computing, face recognition, sparse representation, homomorphic encryption, Oblivious Transfer (OT)

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