Development of Deep Learning-Based Facial Recognition System

2020 ◽  
pp. 45-52
Author(s):  
Hamid Ouanan ◽  
Ahmed Gaga ◽  
Omar Diouri ◽  
Mohammed Ouanan ◽  
Brahim Aksasse
Keyword(s):  
Deep Learning ◽  
Facial Recognition ◽  
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.

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.

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.

Real Time Facial Recognition System

2020 ◽  
pp. 1721-1726
Author(s):  
Ashwini ◽  
Vijay Balaji ◽  
Srivarshini Srinivasan ◽  
Kavya Monisha
Keyword(s):  
Real Time ◽  
Facial Recognition ◽  
Recognition System ◽  
Facial Recognition System

scholarly journals Deep learning based facial expressions recognition system for assisting visually impaired persons

2020 ◽  
Vol 9 (3) ◽  
pp. 1208-1219
Author(s):  
Hendra Kusuma ◽  
Muhammad Attamimi ◽  
Hasby Fahrudin
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Visually Impaired ◽  
Learning Algorithm ◽  
Recognition System ◽  
Verbal Information ◽  
Deep Learning Algorithm ◽  
Facial Recognition System ◽  
Visually Impaired Persons ◽  
Facial Expressions Recognition

In general, a good interaction including communication can be achieved when verbal and non-verbal information such as body movements, gestures, facial expressions, can be processed in two directions between the speaker and listener. Especially the facial expression is one of the indicators of the inner state of the speaker and/or the listener during the communication. Therefore, recognizing the facial expressions is necessary and becomes the important ability in communication. Such ability will be a challenge for the visually impaired persons. This fact motivated us to develop a facial recognition system. Our system is based on deep learning algorithm. We implemented the proposed system on a wearable device which enables the visually impaired persons to recognize facial expressions during the communication. We have conducted several experiments involving the visually impaired persons to validate our proposed system and the promising results were achieved.

scholarly journals Attendance Management System Using Face Recognition Method

2021 ◽  
Vol 2089 (1) ◽  
pp. 012078
Author(s):  
Syed Mansoora ◽  
Giribabu Sadineni ◽  
Shaik Heena Kauser
Keyword(s):  
Classroom Management ◽  
Facial Recognition ◽  
Recognition System ◽  
College Attendance ◽  
Advantages And Disadvantages ◽  
Haar Cascade ◽  
Triplet Loss ◽  
Facial Recognition System ◽  
Work First ◽  
Component Time

Abstract When it comes to classroom management, the attendance check is a critical component. Time-consuming, particularly when it comes to open meetings, is checking attendance by calling names or by handing around a sign-in sheet to make it easier to commit fraud. An implementation of a real-time attendance check is described in this article in great detail facial recognition system and its outcomes. The system must be able to identify a student’s face in order for it to work first snap a photograph of the pupil and save it in a database as a reference for future use. During the event, there were students may be identified by using the webcam, which captures photos of their faces auto-detects faces and selects students with names that are most likely to match, and lastly, depending on the facial recognition findings, an excel file will be updated to reflect attendance. To identify faces in webcam footage, the system uses a pre-trained Haar Cascade model. As a result, a 128-bit FaceNet has been generated by training it to minimise the triplet loss. The dimensions of the facial picture. When two facial pictures have similar encodings If the two facial pictures are from the same student or different. Use of the system as part of a class, and the outcomes have been extremely positive. There has been a poll done to find out more about There are both advantages and disadvantages to using a college attendance system.

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