A Low-Cost Embedded Facial Recognition System for Door Access Control using Deep Learning

2020 ◽  
Author(s):  
Gustavo Orna ◽  
Diego S. Benitez ◽  
Noel Perez
Keyword(s):  
Deep Learning ◽  
Access Control ◽  
Facial Recognition ◽  
Low Cost ◽  
Recognition System ◽  
Facial Recognition System

scholarly journals Design of facial recognition system implemented in an unmanned aerial vehicle for citizen security in Latin America

2019 ◽  
Vol 27 ◽  
pp. 04002
Author(s):  
Diego Herrera ◽  
Hiroki Imamura
Keyword(s):  
Neural Network ◽  
Latin America ◽  
Deep Learning ◽  
Mobile Devices ◽  
Unmanned Aerial Vehicle ◽  
Facial Recognition ◽  
Recognition System ◽  
Criminal Records ◽  
Aerial Vehicle ◽  
Facial Recognition System

In the new technological era, facial recognition has become a central issue for a great number of engineers. Currently, there are a great number of techniques for facial recognition, but in this research, we focus on the use of deep learning. The problems with current facial recognition convection systems are that they are developed in non-mobile devices. This research intends to develop a Facial Recognition System implemented in an unmanned aerial vehicle of the quadcopter type. While it is true, there are quadcopters capable of detecting faces and/or shapes and following them, but most are for fun and entertainment. This research focuses on the facial recognition of people with criminal records, for which a neural network is trained. The Caffe framework is used for the training of a convolutional neural network. The system is developed on the NVIDIA Jetson TX2 motherboard. The design and construction of the quadcopter are done from scratch because we need the UAV for adapt to our requirements. This research aims to reduce violence and crime in Latin America.

scholarly journals Enhancement of Face Recognition using Deep Learning

2019 ◽  
Vol 9 (2) ◽  
pp. 5072-5075
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Feature Detection ◽  
Facial Recognition ◽  
Recognition System ◽  
Detection Capability ◽  
Comparative Result ◽  
Facial Recognition System

Our aim in this paper is to increase the accuracy of existing facial recognition system on a comparative smaller dataset as per the requirements of present day. Namely in sensitive regions. The methodology that has been adopted is by combining more than one algorithms. The feature detection capability of harr cascade along with Ada boost to fetch to Bilinear CNN so that on a comparative smaller dataset can produce comparative result as on bigger dataset.

Low cost automated facial recognition system

2017 ◽  
Author(s):  
M. Yashwanth Sai ◽  
R Vijai Chandra Prasad ◽  
P. R. Niveditha ◽  
T. Sasipraba ◽  
S. Vigneshwari ◽  
...  
Keyword(s):  
Facial Recognition ◽  
Low Cost ◽  
Recognition System ◽  
Facial Recognition System

scholarly journals Spartan Face Mask Detection and Facial Recognition System

Healthcare ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 87
Author(s):  
Ziwei Song ◽  
Kristie Nguyen ◽  
Tien Nguyen ◽  
Catherine Cho ◽  
Jerry Gao
Keyword(s):  
Deep Learning ◽  
Face Detection ◽  
Facial Recognition ◽  
Learning Algorithms ◽  
Face Mask ◽  
Recognition System ◽  
World Health ◽  
Identity Recognition ◽  
Facial Recognition System ◽  
Health Organization

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices.

Facial recognition system for security access control

2021 ◽  
Author(s):  
Daniel A. Florez ◽  
Miguel A. Villa ◽  
Manuel G. Forero ◽  
Carlos A. Lugo
Keyword(s):  
Access Control ◽  
Facial Recognition ◽  
Recognition System ◽  
Facial Recognition System

scholarly journals Facial Recognition System for People with and without Face Mask in Times of the COVID-19 Pandemic

2021 ◽  
Vol 13 (12) ◽  
pp. 6900
Author(s):  
Jonathan S. Talahua ◽  
Jorge Buele ◽  
P. Calvopiña ◽  
José Varela-Aldás
Keyword(s):  
Facial Recognition ◽  
Face Mask ◽  
Recognition System ◽  
Classification Model ◽  
World Health ◽  
The Face ◽  
Recognition Systems ◽  
Facial Recognition System ◽  
Health Organization ◽  
Face Detector

In the face of the COVID-19 pandemic, the World Health Organization (WHO) declared the use of a face mask as a mandatory biosafety measure. This has caused problems in current facial recognition systems, motivating the development of this research. This manuscript describes the development of a system for recognizing people, even when they are using a face mask, from photographs. A classification model based on the MobileNetV2 architecture and the OpenCv’s face detector is used. Thus, using these stages, it can be identified where the face is and it can be determined whether or not it is wearing a face mask. The FaceNet model is used as a feature extractor and a feedforward multilayer perceptron to perform facial recognition. For training the facial recognition models, a set of observations made up of 13,359 images is generated; 52.9% images with a face mask and 47.1% images without a face mask. The experimental results show that there is an accuracy of 99.65% in determining whether a person is wearing a mask or not. An accuracy of 99.52% is achieved in the facial recognition of 10 people with masks, while for facial recognition without masks, an accuracy of 99.96% is obtained.

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