Double-Blinded Finder: a two-side secure children face recognition system

  • Xin Jin
  • Jicheng Lei
  • Shiming GeEmail author
  • Chenggen Song
  • Haoyang Yu
  • Chuanqiang Wu


When taking photos of a suspicious missing child in the street and posting them to the social network is becoming a feasible way to find missing children, the exposure of photos may cause privacy issues. To address this problem, we propose Double-Blinded Finder, an efficient and double-blinded system for finding missing children via low-dimensional multi-attribute representation of child face and blind face matching. To obtain enough knowledge for representing child faces, we build the Labeled Child Face in the Wild dataset, which contains 60K Internet images with 6K unique identities. Based on this dataset, we further train a multi-task deep face model to describe a child face as a 128d fixed-point feature vector and extensive gender and age attributes. Using the keys generated from face descriptors, the face photos from the social network and face representation from the parent(s) of missing children are both encrypted. In addition, we devise inner-production encryption to run blind face matching in the public cloud. In this manner, Double-Blinded Finder can provide efficient face matching while protecting the privacies of both sides: (1) the suspicious missing children side for avoiding the invasion of the human rights, and (2) the true missing children side for preserving the secondary victimization. The experiments show that our system can achieve practical performance of child face matching with negligible leakage of privacy.


Face retrivial Privacy-preserving Double blind Privacy-preserving 



We thank the editors and reviewers. Parts of the results presented in this paper have previously appeared in our previous work [7]. This paper is the extension of the conference short version [7]. This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61772047, 61772513), the Open Project Program of State Key Laboratory of Virtual Reality Technology and Systems, Beihang University (No. VRLAB2019C03), Big Data Application on lmproving Government Governance Capabilities National Engineering Laboratory Open Fund Project (Grant No. W-2018022), and the Fundamental Research Funds for the Central Universities (Grant Nos. 328201906).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Xin Jin
    • 1
    • 2
    • 3
    • 4
  • Jicheng Lei
    • 3
    • 4
  • Shiming Ge
    • 5
    Email author
  • Chenggen Song
    • 6
  • Haoyang Yu
    • 1
  • Chuanqiang Wu
    • 3
    • 4
  1. 1.Beijing Electronic Science and Technology InstituteBeijingChina
  2. 2.State Key Laboratory of CryptologyBeijingChina
  3. 3.CETC Big Data Research Institute Co., Ltd.GuiyangChina
  4. 4.Big Data Application on Improving Government Governance Capabilities National Engineering LaboratoryGuiyangChina
  5. 5.Institute of Information EngineeringChinese Academy of SciencesBeijingChina
  6. 6.OracleChain TechnologyBeijingChina

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