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Privacy-preserving face detection based on linear and nonlinear kernels

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Abstract

With the advance of computer vision, some technologies such as face detection and human detection, have been used widely. However, when processing photos through computer vision technologies, we have to face a privacy-related problem : people do not want their photos to be distributed to others even for taking advantage of computer vision. Since kernel method has been used widely in object classifiers, we proposed a cryptographic algorithm for the kernel method to process encrypted images without decrypting them. So the owner of these images can have them processed by some classifiers belong to other people without leaking the content of these images to these people, and the owner also learns nothing about the classifier. In this paper, we analyze the security, correctness and efficiency of our proposed cryptographic algorithms, then approve the effectiveness of them through some face detection experiments.

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Notes

  1. http://www.mathworks.com/matlabcentral/fileexchange/29834-face-detection-using-support-vector-machine-svm

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Acknowledgments

LiWang is supported partially by NSFC-61300235. This work was supported partially by NSFC-61425024, NSFC-61402223, the Jiangsu Province Double Innovation Talent Program, and NSFC-61321491.

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Authors and Affiliations

  1. Computer Science and Technology Department, Nanjing University, Nanjing, 210046, China

    Li Wang, Jun Jie Shi, Chen Chen & Sheng Zhong

  2. Information and Technology Department, Nanjing Forest University, Nanjing, China

    Li Wang

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  1. Li Wang
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  2. Jun Jie Shi
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  3. Chen Chen
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  4. Sheng Zhong
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Correspondence to Li Wang.

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Wang, L., Shi, J.J., Chen, C. et al. Privacy-preserving face detection based on linear and nonlinear kernels. Multimed Tools Appl 77, 7261–7281 (2018). https://doi.org/10.1007/s11042-017-4632-y

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  • DOI: https://doi.org/10.1007/s11042-017-4632-y

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