A Low-Cost Raspberry Pi-based System for Facial Recognition
Fuente:
Redalyc
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Artículo científico |
| Lenguaje: | en |
| Publicado: |
Universidad EAFIT
2021
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1876447687986905088 |
|---|---|
| author | Cristian Miranda Orostegui |
| author_facet | Cristian Miranda Orostegui |
| contents | A Low-Cost Raspberry Pi-based System for Facial Recognition Cristian Miranda Orostegui Alejandro Navarro Luna Andrés Manjarrés García Carlos Augusto Fajardo Ariza Ingeniería faceNet googLeNet Deep learning embedded systems facial recognition Deep learning has become increasingly popular and widely applied to computer vision systems. Over the years, researchers have developed various deep learning architectures to solve different kinds of problems. However, these networks are power-hungry and require high-performance computing (i.e., GPU, TPU, etc.) to run appropriately. Moving computation to the cloud may result in traffic, latency, and privacy issues. Edge computing can solve these challenges by moving the computing closer to the edge where the data is generated. One major challenge is to fit the high resource demands of deep learning in less powerful edge computing devices. In this research, we present an implementation of an embedded facial recognition system on a low cost Raspberry Pi, which is based on the FaceNet architecture. For this implementation it was required the development of a library in C++, which allows the deployment of the inference of the Neural Network Architecture. The system had an accuracy and precision of 77.38% and 81.25%, respectively. The time of execution of the program is 11 seconds and it consumes 46 [kB] of RAM. The resulting system could be utilized as a stand-alone access control system. The implemented model and library are released at https: //github.com/cristianMiranda-Oro/FaceNet_EmbeddedSystem. 2021 artículo científico 1794-9165 https://www.redalyc.org/articulo.oa?id=83582470004 en http://www.redalyc.org/revista.oa?id=835 Ingeniería y Ciencia application/pdf Universidad EAFIT Ingeniería y Ciencia (Colombia) Num.34 Vol.17 |
| format | Artículo científico |
| id | redalyc_83582470004 |
| institution | Redalyc |
| language | en |
| publishDate | 2021 |
| publisher | Universidad EAFIT |
| spellingShingle | A Low-Cost Raspberry Pi-based System for Facial Recognition Cristian Miranda Orostegui Ingeniería faceNet googLeNet Deep learning embedded systems facial recognition A Low-Cost Raspberry Pi-based System for Facial Recognition Cristian Miranda Orostegui Alejandro Navarro Luna Andrés Manjarrés García Carlos Augusto Fajardo Ariza Ingeniería faceNet googLeNet Deep learning embedded systems facial recognition Deep learning has become increasingly popular and widely applied to computer vision systems. Over the years, researchers have developed various deep learning architectures to solve different kinds of problems. However, these networks are power-hungry and require high-performance computing (i.e., GPU, TPU, etc.) to run appropriately. Moving computation to the cloud may result in traffic, latency, and privacy issues. Edge computing can solve these challenges by moving the computing closer to the edge where the data is generated. One major challenge is to fit the high resource demands of deep learning in less powerful edge computing devices. In this research, we present an implementation of an embedded facial recognition system on a low cost Raspberry Pi, which is based on the FaceNet architecture. For this implementation it was required the development of a library in C++, which allows the deployment of the inference of the Neural Network Architecture. The system had an accuracy and precision of 77.38% and 81.25%, respectively. The time of execution of the program is 11 seconds and it consumes 46 [kB] of RAM. The resulting system could be utilized as a stand-alone access control system. The implemented model and library are released at https: //github.com/cristianMiranda-Oro/FaceNet_EmbeddedSystem. 2021 artículo científico 1794-9165 https://www.redalyc.org/articulo.oa?id=83582470004 en http://www.redalyc.org/revista.oa?id=835 Ingeniería y Ciencia application/pdf Universidad EAFIT Ingeniería y Ciencia (Colombia) Num.34 Vol.17 |
| title | A Low-Cost Raspberry Pi-based System for Facial Recognition |
| topic | Ingeniería faceNet googLeNet Deep learning embedded systems facial recognition |
| url | https://www.redalyc.org/articulo.oa?id=83582470004 |