An efficient deep neural networks training framework for robust face recognition

2017 ◽  
Author(s):  
Canping Su ◽  
Yan Yan ◽  
Si Chen ◽  
Hanzi Wang
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Deep Neural Networks ◽  
Robust Face Recognition

Deep Neural Networks for Face Recognition: Pairwise Optimisation

2018 ◽  
pp. 103-110
Author(s):  
Elitsa Popova ◽  
Athanasios Athanasopoulos ◽  
Efraim Ie ◽  
Nikolaos Christou ◽  
Ndifreke Nyah
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Deep Neural Networks

scholarly journals Illumination-robust face recognition based on deep convolutional neural networks architectures

2020 ◽  
Vol 18 (2) ◽  
pp. 1015
Author(s):  
Ridha Ilyas Bendjillali ◽  
Mohammed Beladgham ◽  
Khaled Merit ◽  
Abdelmalik Taleb-Ahmed
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Convolutional Neural Networks ◽  
Feature Learning ◽  
Histogram Equalization ◽  
Detection Algorithm ◽  
Deep Convolutional Neural Networks ◽  
Biometric Technology ◽  
Equalization Algorithm ◽  
Robust Face Recognition

<p><span>In the last decade, facial recognition techniques are considered the most important fields of research in biometric technology. In this research paper, we present a Face Recognition (FR) system divided into three steps: The Viola-Jones face detection algorithm, facial image enhancement using Modified Contrast Limited Adaptive Histogram Equalization algorithm (M-CLAHE), and feature learning for classification. For learning the features followed by classification we used VGG16, ResNet50 and Inception-v3 Convolutional Neural Networks (CNN) architectures for the proposed system. Our experimental work was performed on the Extended Yale B database and CMU PIE face database. Finally, the comparison with the other methods on both databases shows the robustness and effectiveness of the proposed approach. Where the Inception-v3 architecture has achieved a rate of 99, 44% and 99, 89% respectively.</span></p>

scholarly journals Hyperspectral face recognition based on sparse spectral attention deep neural networks

2020 ◽  
Vol 28 (24) ◽  
pp. 36286
Author(s):  
Zhihua Xie ◽  
Yi Li ◽  
Jieyi Niu ◽  
Ling Shi ◽  
Zhipeng Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Deep Neural Networks

scholarly journals Designing Automated Deployment Strategies of Face Recognition Solutions in Heterogeneous IoT Platforms

Information ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 532
Author(s):  
Unai Elordi ◽  
Chiara Lunerti ◽  
Luis Unzueta ◽  
Jon Goenetxea ◽  
Nerea Aranjuelo ◽  
...  
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
User Interaction ◽  
Biometric Data ◽  
Optimal Deployment ◽  
Iot Platforms ◽  
Iot Devices ◽  
Hardware Platforms

In this paper, we tackle the problem of deploying face recognition (FR) solutions in heterogeneous Internet of Things (IoT) platforms. The main challenges are the optimal deployment of deep neural networks (DNNs) in the high variety of IoT devices (e.g., robots, tablets, smartphones, etc.), the secure management of biometric data while respecting the users’ privacy, and the design of appropriate user interaction with facial verification mechanisms for all kinds of users. We analyze different approaches to solving all these challenges and propose a knowledge-driven methodology for the automated deployment of DNN-based FR solutions in IoT devices, with the secure management of biometric data, and real-time feedback for improved interaction. We provide some practical examples and experimental results with state-of-the-art DNNs for FR in Intel’s and NVIDIA’s hardware platforms as IoT devices.

Sign in / Sign up

Export Citation Format

Share Document