An effective face recognition system based on Cloud based IoT with a deep learning model

2021 ◽  
Vol 81 ◽  
pp. 103726
Author(s):  
Deepika Chauhan ◽  
Ashok Kumar ◽  
Pradeep Bedi ◽  
Vijay Anant Athavale ◽  
D. Veeraiah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Recognition System ◽  
Learning Model ◽  
Face Recognition System ◽  
Deep Learning Model

scholarly journals Design of a Face Recognition System based on Convolutional Neural Network (CNN)

2020 ◽  
Vol 10 (3) ◽  
pp. 5608-5612 ◽  
Author(s):  
Y. Said ◽  
M. Barr ◽  
H. E. Ahmed
Keyword(s):  
Face Recognition ◽  
State Of The Art ◽  
Recognition System ◽  
Great Variability ◽  
Artificial Vision ◽  
Surveillance Systems ◽  
Distributed Network ◽  
Biometric System ◽  
Face Recognition System ◽  
Deep Learning Model

Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.

scholarly journals Masked Face Analysis via Multi-Task Deep Learning

2021 ◽  
Vol 7 (10) ◽  
pp. 204
Author(s):  
Vatsa S. Patel ◽  
Zhongliang Nie ◽  
Trung-Nghia Le ◽  
Tam V. Nguyen
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Face Recognition ◽  
Face Mask ◽  
Learning Model ◽  
Unified Framework ◽  
Improve Performance ◽  
Face Analysis ◽  
And Gender ◽  
Deep Learning Model

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

scholarly journals Deep Learning Model Based on Mobile-Net with Haar-like Algorithm for Masked Face Recognition at Nuclear Facilities

2021 ◽  
Vol 10 (7) ◽  
pp. 18-23
Author(s):  
Nadia.M. Nawwar* ◽  
Kasban . ◽  
Salama May
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Face Mask ◽  
Learning Model ◽  
Nuclear Facility ◽  
Nuclear Facilities ◽  
Novel Technique ◽  
The World ◽  
The Individual ◽  
Deep Learning Model

During the spread of the COVID-I9 pandemic in early 2020, the WHO organization advised all people in the world to wear face-mask to limit the spread of COVID-19. Many facilities required that their employees wear face-mask. For the safety of the facility, it was mandatory to recognize the identity of the individual wearing the mask. Hence, face recognition of the masked individuals was required. In this research, a novel technique is proposed based on a mobile-net and Haar-like algorithm for detecting and recognizing the masked face. Firstly, recognize the authorized person that enters the nuclear facility in case of wearing the masked-face using mobile-net. Secondly, applying Haar-like features to detect the retina of the person to extract the boundary box around the retina compares this with the dataset of the person without the mask for recognition. The results of the proposed modal, which was tested on a dataset from Kaggle, yielded 0.99 accuracies, a loss of 0.08, F1.score 0.98.

A Deep Learning based Approach for Real Time Face Recognition System

2021 ◽  
Author(s):  
Tanusree Das Tithy ◽  
Soarov Chakraborty ◽  
Rabaya Islam ◽  
Abdul Aziz
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Recognition System ◽  
Face Recognition System
Sign in / Sign up

Export Citation Format

Share Document