A Low-Cost Raspberry Pi-based System for 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...
| Autores: | , , , |
|---|---|
| Tipo de documento: | artigo |
| Estado: | Versão publicada |
| Data de publicação: | 2021 |
| País: | Colombia |
| Recursos: | Universidad EAFIT |
| Repositório: | Repositorio EAFIT |
| Idioma: | inglês |
| OAI Identifier: | oai:repository.eafit.edu.co:10784/31020 |
| Acesso em linha: | http://hdl.handle.net/10784/31020 |
| Access Level: | Acceso aberto |
| Palavra-chave: | Deep learning facial recognition embedded systems FaceNet GoogLeNet Labeled Faces in the Wild reconocimiento facial sistemas embebidos systemsFaceNet FaceNetGoogLeNet |
| Resumo: | 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 |
|---|