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...
| Autores: | ; ; ; ; ; ; |
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| Tipo de documento: | Artículo |
| Fecha de publicación: | 2021 |
| País: | España |
| Institución: | Universidad del País Vasco |
| Repositorio: | Addi. Archivo Digital para la Docencia y la Investigación |
| OAI Identifier: | oai:addi.ehu.eus:10810/54755 |
| Acceso en línea: | http://hdl.handle.net/10810/54755 |
| Nivel de acceso: | Acceso abierto |
| Palabra clave: | knowledge-driven approach face recognition deep neural networks Internet of Things user interaction |
| _version_ | 1878729971600457728 |
|---|---|
| author | Elordi Hidalgo, Unai |
| author2 | Lunerti, Chiara Unzueta Irurtia, Luis Goenetxea Imaz, Jon Aranjuelo Ansa, Nerea Bertelsen, Alvaro Arganda Carreras, Ignacio |
| author2_role | author author author author author author |
| author_browse | Aranjuelo Ansa, Nerea Arganda Carreras, Ignacio Bertelsen, Alvaro Elordi Hidalgo, Unai Goenetxea Imaz, Jon Lunerti, Chiara Unzueta Irurtia, Luis |
| author_facet | Elordi Hidalgo, Unai Lunerti, Chiara Unzueta Irurtia, Luis Goenetxea Imaz, Jon Aranjuelo Ansa, Nerea Bertelsen, Alvaro Arganda Carreras, Ignacio |
| author_role | author |
| collection | Addi. Archivo Digital para la Docencia y la Investigación |
| description | 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. |
| eu_rights_str_mv | openAccess |
| format | article |
| id | ES_12beff68bd384890f4f9a24eb55ffc96 |
| institution | Universidad del País Vasco |
| instname_str | Universidad del País Vasco |
| network_acronym_str | ES |
| network_name_str | España |
| oai_identifier_str | oai:addi.ehu.eus:10810/54755 |
| publishDate | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI |
| publisherStr | MDPI |
| reponame_str | Addi. Archivo Digital para la Docencia y la Investigación |
| spelling | Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms Elordi Hidalgo, Unai Lunerti, Chiara Unzueta Irurtia, Luis Goenetxea Imaz, Jon Aranjuelo Ansa, Nerea Bertelsen, Alvaro Arganda Carreras, Ignacio knowledge-driven approach face recognition deep neural networks Internet of Things user interaction 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. This work was supported by the SHAPES project, which has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 857159, and in part by the Spanish Centre for the Development of Industrial Technology (CDTI) through the Project ÉGIDA—RED DE EXCELENCIA EN TECNOLOGIAS DE SEGURIDAD Y PRIVACIDAD under Grant CER20191012. MDPI http://hdl.handle.net/10810/54755 |
| spellingShingle | Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms Elordi Hidalgo, Unai knowledge-driven approach face recognition deep neural networks Internet of Things user interaction |
| title | Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms |
| title_full | Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms |
| title_fullStr | Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms |
| title_full_unstemmed | Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms |
| title_short | Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms |
| title_sort | Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms |
| topic | knowledge-driven approach face recognition deep neural networks Internet of Things user interaction |
| topic_facet | knowledge-driven approach face recognition deep neural networks Internet of Things user interaction |
| url | http://hdl.handle.net/10810/54755 |