ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Jiankang Deng,Jia Guo,Niannan Xue,Stefanos Zafeiriou +3 more
- pp 4690-4699
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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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Journal ArticleDOI
WebFace260M: A Benchmark for Million-Scale Deep Face Recognition
Zheng H. Zhu,Guan Huang,Jiankang Deng,Yun Ye,Junjie Huang,Xinze Chen,Jiagang Zhu,Tian Yang,Dalong Du,Jiwen Lu,Jie Zhou +10 more
TL;DR: A new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identity/42M face training data, as well as an elaborately designed time-constrained evaluation protocol, shows enormous potential on standard, masked and unbiased face recognition scenarios.
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A Systematic Comparison of Depth Map Representations for Face Recognition
TL;DR: In this paper, the authors compare different depth data representations (depth and normal images, voxels, point clouds), deep models (two-dimensional and three-dimensional Convolutional Neural Networks, PointNet-based networks), and pre-processing and normalization techniques in order to determine the configuration that maximizes the recognition accuracy and is capable of generalizing better on unseen data and novel acquisition settings.
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Towards Flops-constrained Face Recognition
TL;DR: Wang et al. as mentioned in this paper proposed an efficient polyface based on the Flops constraint, a novel loss function called ArcNegFace, and a novel frame aggregation method called QAN++ to achieve state-of-the-art results.
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Efficient Lightweight Attention Network for Face Recognition
TL;DR: Zhang et al. as discussed by the authors proposed Efficient Lightweight Attention Networks (ELANet) to address the challenge brought by the impacts of poses and ages on face recognition performance, where spatial attention is used to capture important locally similar patches and channel attention is employed to focus on features with different levels of importance.
Proceedings ArticleDOI
Lightweight Low-Resolution Face Recognition for Surveillance Applications
Yoanna Martínez-Díaz,Heydi Méndez-Vázquez,Luis S. Luevano,Leonardo Chang,Miguel González-Mendoza +4 more
TL;DR: Inspired by the compactness and computation efficiency of lightweight deep face networks and their high accuracy on general face recognition tasks, this work proposes to benchmark two recently introduced lightweight face models on low-resolution surveillance imagery to enable efficient system deployment.
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