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...

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Autores:
Miranda Orostegui, Cristian
Navarro Luna, Alejandro
Manjarés García, Alejandro
Fajardo Ariza, Carlos Augusto
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad EAFIT
Repositorio:
Repositorio EAFIT
Idioma:
eng
OAI Identifier:
oai:repository.eafit.edu.co:10784/31020
Acceso en línea:
http://hdl.handle.net/10784/31020
Palabra clave:
Deep learning
facial recognition
embedded systems
FaceNet
GoogLeNet
Labeled Faces in the Wild
Deep learning
reconocimiento facial
sistemas embebidos
systemsFaceNet
FaceNetGoogLeNet
Labeled Faces in the Wild
Rights
License
Acceso abierto
Description
Summary: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