Improved modality-invariant feature learning for degraded face recognition

Author(s):  
Zhifang Yang ◽  
Zhengxin Song ◽  
Ke Wen
2014 ◽  
Vol 74 (24) ◽  
pp. 11281-11295 ◽  
Author(s):  
Mohamed Anouar Borgi ◽  
Maher El’Arbi ◽  
Demetrio Labate ◽  
Chokri Ben Amar

Author(s):  
Ridha Ilyas Bendjillali ◽  
Mohammed Beladgham ◽  
Khaled Merit ◽  
Abdelmalik Taleb-Ahmed

<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>


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

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 20818-20827 ◽  
Author(s):  
Zhi Zhang ◽  
Ruoqiao Jiang ◽  
Shaohui Mei ◽  
Shun Zhang ◽  
Yifan Zhang

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

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