Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms

In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secu...

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Bibliographic Details
Published in:Information (Basel) 2021-12, Vol.12 (12), p.532
Main Authors: Elordi, Unai, Lunerti, Chiara, Unzueta, Luis, Goenetxea, Jon, Aranjuelo, Nerea, Bertelsen, Alvaro, Arganda-Carreras, Ignacio
Format: Article
Language:English
Subjects:
Artificial neural networks
Automation
Biometrics
Computer industry
Data encryption
Data integrity
Decision making
deep neural networks
Dictionaries
Face recognition
Facial recognition technology
Internet of Things
knowledge-driven approach
Platforms
Privacy
Smartphones
Tablet computers
Training
user interaction
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Summary:In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices.
ISSN:2078-2489
2078-2489
DOI:10.3390/info12120532