Privacy Preserving Face Identification in the Cloud through Sparse Representation

Privacy Preserving Face Identification in the Cloud through Sparse Representation

Xin Jin1,∗ , Yan Liu1,2 , Xiaodong Li1, , Geng Zhao1 , Yingya Chen1 , and Kui Guo

Abstract

   Nowadays, with tremendous visual media stored and even processed in the cloud, the privacy of visual media is also exposed to the cloud. In this paper we propose a private face identification method based on sparse representation. The identification is done in a secure way which protects both the privacy of the subjects and the confidentiality of the database. The face identification server in the cloud contains a list of registered faces. The surveillance client captures a face image and require the server to identify if the client face matches one of the suspects, but otherwise reveals no information to neither of the two parties. This is the first work that introduces sparse representation to the secure protocol of private face identification, which reduces the dimension of the face representation vector and avoid the patch based attack of a previous work. Besides, we introduce a secure Euclidean distance algorithm for the secure protocol. The experimental results reveal that the cloud server can return the identification results to the surveillance client without knowing anything about the client face image.

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