Context-Aware Local Binary Feature Learning An Approach For Face Recognition

2019 ◽  
Vol 7 (6) ◽  
pp. 462-465
Author(s):  
Sushmitamai K. Ahire ◽  
Nilesh R. Wankhade
Keyword(s):  
Face Recognition ◽  
Feature Learning ◽  
Context Aware ◽  
Binary Feature

Context-Aware Local Binary Feature Learning for Face Recognition

2018 ◽  
Vol 40 (5) ◽  
pp. 1139-1153 ◽  
Author(s):  
Yueqi Duan ◽  
Jiwen Lu ◽  
Jianjiang Feng ◽  
Jie Zhou
Keyword(s):  
Face Recognition ◽  
Feature Learning ◽  
Context Aware ◽  
Binary Feature

Regularized directional feature learning for face recognition

2014 ◽  
Vol 74 (24) ◽  
pp. 11281-11295 ◽  
Author(s):  
Mohamed Anouar Borgi ◽  
Maher El’Arbi ◽  
Demetrio Labate ◽  
Chokri Ben Amar
Keyword(s):  
Face Recognition ◽  
Feature Learning

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>

Progressive Context-aware Graph Feature Learning for Target Re-identification

2022 ◽  
pp. 1-1
Author(s):  
Min Cao ◽  
Cong Ding ◽  
Chen Chen ◽  
Hao Dou ◽  
Xiyuan Hu ◽  
...  
Keyword(s):  
Feature Learning ◽  
Context Aware

Hierarchical discriminant feature learning for cross-modal face recognition

2019 ◽  
Vol 79 (45-46) ◽  
pp. 33483-33502 ◽  
Author(s):  
Xiaolin Xu ◽  
Yidong Li ◽  
Yi Jin
Keyword(s):  
Face Recognition ◽  
Feature Learning

Uncorrelated multi-set feature learning for color face recognition

2016 ◽  
Vol 60 ◽  
pp. 630-646 ◽  
Author(s):  
Fei Wu ◽  
Xiao-Yuan Jing ◽  
Xiwei Dong ◽  
Qi Ge ◽  
Songsong Wu ◽  
...  
Keyword(s):  
Face Recognition ◽  
Feature Learning ◽  
Color Face Recognition
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