A Discriminative Feature Learning Approach for Deep Face Recognition

2016 ◽  
pp. 499-515 ◽  
Author(s):  
Yandong Wen ◽  
Kaipeng Zhang ◽  
Zhifeng Li ◽  
Yu Qiao
Keyword(s):  
Face Recognition ◽  
Feature Learning ◽  
Learning Approach ◽  
Discriminative Feature

A feature learning approach for face recognition with robustness to noisy label based on top-N prediction

2019 ◽  
Vol 330 ◽  
pp. 48-55 ◽  
Author(s):  
Menglong Yang ◽  
Feihu Huang ◽  
Xuebin Lv
Keyword(s):  
Face Recognition ◽  
Feature Learning ◽  
Learning Approach

scholarly journals Mis-Classified Vector Guided Softmax Loss for Face Recognition

2020 ◽  
Vol 34 (07) ◽  
pp. 12241-12248 ◽  
Author(s):  
Xiaobo Wang ◽  
Shifeng Zhang ◽  
Shuo Wang ◽  
Tianyu Fu ◽  
Hailin Shi ◽  
...  
Keyword(s):  
Face Recognition ◽  
Loss Function ◽  
State Of The Art ◽  
Feature Learning ◽  
Ground Truth ◽  
Significant Progress ◽  
Deep Convolutional Neural Networks ◽  
Face Features ◽  
Discriminative Feature ◽  
Feature Mining

Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination. To this end, several margin-based (e.g., angular, additive and additive angular margins) softmax loss functions have been proposed to increase the feature margin between different classes. However, despite great achievements have been made, they mainly suffer from three issues: 1) Obviously, they ignore the importance of informative features mining for discriminative learning; 2) They encourage the feature margin only from the ground truth class, without realizing the discriminability from other non-ground truth classes; 3) The feature margin between different classes is set to be same and fixed, which may not adapt the situations very well. To cope with these issues, this paper develops a novel loss function, which adaptively emphasizes the mis-classified feature vectors to guide the discriminative feature learning. Thus we can address all the above issues and achieve more discriminative face features. To the best of our knowledge, this is the first attempt to inherit the advantages of feature margin and feature mining into a unified loss function. Experimental results on several benchmarks have demonstrated the effectiveness of our method over state-of-the-art alternatives. Our code is available at http://www.cbsr.ia.ac.cn/users/xiaobowang/.

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
Sign in / Sign up

Export Citation Format

Share Document