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

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 th...

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Bibliographic Details
Published in:The Journal of supercomputing 2018-12, Vol.74 (12), p.6729-6741
Main Authors: Oh, Seon Ho, Kim, Geon-Woo, Lim, Kyung-Soo
Format: Article
Language:English
Subjects:
Artificial neural networks
Compilers
Computer Science
Embedded systems
Face recognition
Facial recognition technology
Feature recognition
Internet of Things
Interpreters
Neural networks
Object recognition
Processor Architectures
Programming Languages
State of the art
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Summary: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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-017-2198-0