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Erschienen in: The Journal of Supercomputing 12/2018

28.11.2017

Compact deep learned feature-based face recognition for Visual Internet of Things

verfasst von: Seon Ho Oh, Geon-Woo Kim, Kyung-Soo Lim

Erschienen in: The Journal of Supercomputing | Ausgabe 12/2018

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Abstract

The Visual Internet of Things has received much attention in recent years due to its ability to get the object location via image information of the scene, attach the visual label to the object, and then return information of scene objects to the network. In particular, face recognition is one of the most suitable means to Visual IoT because face feature is inherent label for human being. However, current state-of-the-art face recognition methods based on huge deep neural networks are difficult to apply in the embedded platform for Visual IoT due to the lack of computational resources. To solve this problem, we present compact deep neural network-based face recognition method for Visual Internet of Things. The proposed method has a low model complexity to operate in an embedded environment while using deep neural networks, which is strong against posture and illumination changes. We show competitive accuracy and performance results for the LFW verification benchmark and the collected mobile face recognition dataset. Additionally, we demonstrate that the implementation of the proposed system can be run in real time on the Android-based mobile embedded platform.

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Metadaten
Titel
Compact deep learned feature-based face recognition for Visual Internet of Things
verfasst von
Seon Ho Oh
Geon-Woo Kim
Kyung-Soo Lim
Publikationsdatum
28.11.2017
Verlag
Springer US
Erschienen in
The Journal of Supercomputing / Ausgabe 12/2018
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-017-2198-0

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