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http://dx.doi.org/10.17661/jkiiect.2021.14.5.430

DCGAN-based Compensation for Soft Errors in Face Recognition systems based on a Cross-layer Approach  

Cho, Young-Hwan (Dept. of Electronic Engineering, Kookmin University)
Kim, Do-Yun (Dept. of Electronic Engineering, Kookmin University)
Lee, Seung-Hyeon (Dept. of Electronic Engineering, Kookmin University)
Jeong, Gu-Min (Autonomous a2z)
Publication Information
The Journal of Korea Institute of Information, Electronics, and Communication Technology / v.14, no.5, 2021 , pp. 430-437 More about this Journal
Abstract
In this paper, we propose a robust face recognition method against soft errors with a deep convolutional generative adversarial network(DCGAN) based compensation method by a cross-layer approach. When soft-errors occur in block data of JPEG files, these blocks can be decoded inappropriately. In previous results, these blocks have been replaced using a mean face, thereby improving recognition ratio to a certain degree. This paper uses a DCGAN-based compensation approach to extend the previous results. When soft errors are detected in an embedded system layer using parity bit checkers, they are compensated in the application layer using compensated block data by a DCGAN-based compensation method. Regarding soft errors and block data loss in facial images, a DCGAN architecture is redesigned to compensate for the block data loss. Simulation results show that the proposed method effectively compensates for performance degradation due to soft errors.
Keywords
Cross-layer approach; DCGAN; Face recognition; Soft error; Yale Face Database;
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