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Open AccessArticle

WGAN-E: A Generative Adversarial Networks for Facial Feature Security

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
O’Neill School of Public and Environmental Affairs, Indiana University Bloomington, Bloomington, IN 47405, USA
Department of Mathematics and Computer Science, Northeastern State University, Tahlequah, OK 74464, USA
Author to whom correspondence should be addressed.
Electronics 2020, 9(3), 486;
Received: 17 February 2020 / Revised: 9 March 2020 / Accepted: 10 March 2020 / Published: 15 March 2020
(This article belongs to the Special Issue Data Analysis in Intelligent Communication Systems)
Artificial intelligence technology plays an increasingly important role in human life. For example, distinguishing different people is an essential capability of many intelligent systems. To achieve this, one possible technical means is to perceive and recognize people by optical imaging of faces, so-called face recognition technology. After decades of research and development, especially the emergence of deep learning technology in recent years, face recognition has made great progress with more and more applications in the fields of security, finance, education, social security, etc. The field of computer vision has become one of the most successful branch areas. With the wide application of biometrics technology, bio-encryption technology came into being. Aiming at the problems of classical hash algorithm and face hashing algorithm based on Multiscale Block Local Binary Pattern (MB-LBP) feature improvement, this paper proposes a method based on Generative Adversarial Networks (GAN) to encrypt face features. This work uses Wasserstein Generative Adversarial Networks Encryption (WGAN-E) to encrypt facial features. Because the encryption process is an irreversible one-way process, it protects facial features well. Compared with the traditional face hashing algorithm, the experimental results show that the face feature encryption algorithm has better confidentiality. View Full-Text
Keywords: facial feature; generative adversarial networks; Wasserstein GAN; face recognition; neurocryptography facial feature; generative adversarial networks; Wasserstein GAN; face recognition; neurocryptography
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Wu, C.; Ju, B.; Wu, Y.; Xiong, N.N.; Zhang, S. WGAN-E: A Generative Adversarial Networks for Facial Feature Security. Electronics 2020, 9, 486.

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