Large Margin Coupled Feature Learning for cross-modal face recognition

Learning large-margin face features whose intra-class variance is small and inter-class diversity is one of important challenges in feature learning applying Deep Convolutional Neural Networks (DCNNs) for face recognition. Recently, an appealing line of research is to incorporate an angular margin in the original softmax loss functions for obtaining discriminative deep features during the training of DCNNs. In this paper we propose a novel loss function, termed as double additive margin Softmax loss (DAM-Softmax). The presented loss has a clearer geometrical explanation and can obtain highly discriminative features for face recognition. Extensive experimental evaluation of several recent state-of-the-art softmax loss functions are conducted on the relevant face recognition benchmarks, CASIA-Webface, LFW, CALFW, CPLFW, and CFP-FP. We show that the proposed loss function consistently outperforms the state-of-the-art.

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

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i6.462465 ◽

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

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Pose-robust Feature Learning for Pose-Invariant Face Recognition

2020 IEEE 5th International Conference on Signal and Image Processing (ICSIP) ◽

10.1109/icsip49896.2020.9339444 ◽

2020 ◽

Author(s):

Yigan Li ◽

Zhaohui Wang ◽

Jiawei Hou

Keyword(s):

Face Recognition ◽

Feature Learning

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Regularized directional feature learning for face recognition

Multimedia Tools and Applications ◽

10.1007/s11042-014-2228-3 ◽

2014 ◽

Vol 74 (24) ◽

pp. 11281-11295 ◽

Cited By ~ 4

Author(s):

Mohamed Anouar Borgi ◽

Maher El’Arbi ◽

Demetrio Labate ◽

Chokri Ben Amar

Keyword(s):

Face Recognition ◽

Feature Learning

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

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v18.i2.pp1015-1027 ◽

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>

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