ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Jiankang Deng,Jia Guo,Niannan Xue,Stefanos Zafeiriou +3 more
- pp 4690-4699
TLDR
This paper presents arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks, and shows that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead.Abstract:
One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large-scale face recognition is the design of appropriate loss functions that can enhance the discriminative power. Centre loss penalises the distance between deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the linear transformation matrix in the last fully connected layer can be used as a representation of the class centres in the angular space and therefore penalises the angles between deep features and their corresponding weights in a multiplicative way. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to maximise face class separability. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to its exact correspondence to geodesic distance on a hypersphere. We present arguably the most extensive experimental evaluation against all recent state-of-the-art face recognition methods on ten face recognition benchmarks which includes a new large-scale image database with trillions of pairs and a large-scale video dataset. We show that ArcFace consistently outperforms the state of the art and can be easily implemented with negligible computational overhead. To facilitate future research, the code has been made available.read more
Citations
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Delving into Inter-Image Invariance for Unsupervised Visual Representations.
TL;DR: Through carefully-designed comparisons and analysis, this work proposes a unified framework that supports the integration of unsupervised intra- and inter-image invariance learning from three main constituting components: pseudo-label maintenance, sampling strategy, and decision boundary design.
Proceedings ArticleDOI
MFR 2021: Masked Face Recognition Competition
Fadi Boutros,Naser Damer,Jan Niklas Kolf,Kiran B. Raja,Florian Kirchbuchner,Raghavendra Ramachandra,Arjan Kuijper,Pengcheng Fang,Chao Zhang,Fei Wang,David Montero,Naiara Aginako,Basilio Sierra,Marcos Nieto,Mustafa Ekrem Erakin,Ugur Demir,Hazim Kemal Ekenel,Asaki Kataoka,Kohei Ichikawa,Shizuma Kubo,Jie Zhang,Mingjie He,Dan Han,Shiguang Shan,Klemen Grm,Vitomir Struc,Sachith Seneviratne,Nuran Kasthuriarachchi,Sanka Rasnayaka,Pedro C. Neto,Ana F. Sequeira,Joao Ribeiro Pinto,Mohsen Saffari,Jaime S. Cardoso +33 more
TL;DR: The Masked Face Recognition Competition (MFR) as discussed by the authors was held within the 2021 International Joint Conference on Biometrics (IJCB 2021) and attracted a total of 10 participating teams with valid submissions.
Book ChapterDOI
FairFace Challenge at ECCV 2020: Analyzing Bias in Face Recognition
TL;DR: The 2020 ChaLearn Looking at People Fair Face Recognition and Analysis Challenge as mentioned in this paper evaluated accuracy and bias in gender and skin colour of submitted algorithms on the task of 1:1 face verification in the presence of other confounding attributes.
Proceedings ArticleDOI
Adversarial Learning With Margin-Based Triplet Embedding Regularization
Yaoyao Zhong,Weihong Deng +1 more
TL;DR: Li et al. as discussed by the authors proposed to improve the local smoothness of the representation space, by integrating a margin-based triplet embedding regularization term into the classification objective, so that the obtained models learn to resist adversarial examples.
Pseudo RGB-D Face Recognition
TL;DR: Zhang et al. as mentioned in this paper proposed a pseudo RGB-D face recognition framework and provided data-driven ways to generate the depth maps from 2D face images, which can teach computers to generate correct answers by training.
References
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TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martín Abadi,Ashish Agarwal,Paul Barham,Eugene Brevdo,Zhifeng Chen,Craig Citro,Greg S. Corrado,Andy Davis,Jeffrey Dean,Matthieu Devin,Sanjay Ghemawat,Ian Goodfellow,Andrew Harp,Geoffrey Irving,Michael Isard,Yangqing Jia,Rafal Jozefowicz,Lukasz Kaiser,Manjunath Kudlur,Josh Levenberg,Dan Mané,Rajat Monga,Sherry Moore,Derek G. Murray,Chris Olah,Mike Schuster,Jonathon Shlens,Benoit Steiner,Ilya Sutskever,Kunal Talwar,Paul A. Tucker,Vincent Vanhoucke,Vijay K. Vasudevan,Fernanda B. Viégas,Oriol Vinyals,Pete Warden,Martin Wattenberg,Martin Wicke,Yuan Yu,Xiaoqiang Zheng +39 more
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