Robust Face Recognition Against Soft-errors Using a Cross-layer Approach

Gu-Min Jeong, Chang-Woo Park, Sang-Il Choi, Kyoungwoo Lee, Nikil Dutt

Abstract


Recently, soft-errors, temporary bit toggles in memory systems, have become increasingly important. Although soft-errors are not critical to the stability of recognition systems or multimedia systems, they can significantly degrade the system performance. Considering these facts, in this paper, we propose a novel method for robust face recognition against soft-errors using a cross layer approach. To attenuate the effect of soft-errors in the face recognition system, they are detected in the embedded system layer by using a parity bit checker and compensated in the application layer by using a mean face. We present the soft-error detection module for face recognition and the compensation module based on the mean face of the facial images. Simulation results show that the proposed system effectively compensates for the performance degradation due to soft errors and improves the performance by 2.11 % in case of the Yale database and by 10.43 % in case of the ORL database on average as compared to that with the soft-errors induced.

Keywords


Soft-error, Face recognition, Cross-layer approach, Mean face

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References


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DOI: https://doi.org/10.15837/ijccc.2016.5.2020



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