ArcFace: Additive Angular Margin Loss for Deep Face Recognition
15 Jun 2019-pp 4690-4699
TL;DR: 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.
Citations
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05 May 2022
TL;DR: Surprisingly, the experiments demonstrate that, although visually more appealing, morphs based on StyleGAN 2 do not pose a significant threat to the state to face recognition systems, as these morphs were outmatched by the simple morphs that are based facial landmarks.
Abstract: Morphing attacks are a threat to biometric systems where the biometric reference in an identity document can be altered. This form of attack presents an important issue in applications relying on identity documents such as border security or access control. Research in generation of face morphs and their detection is developing rapidly, however very few datasets with morphing attacks and open-source detection toolkits are publicly available. This paper bridges this gap by providing two datasets and the corresponding code for four types of morphing attacks: two that rely on facial landmarks based on OpenCV and FaceMorpher, and two that use StyleGAN 2 to generate synthetic morphs. We also conduct extensive experiments to assess the vulnerability of four state-of-the-art face recognition systems, including FaceNet, VGG-Face, ArcFace, and ISV. Surprisingly, the experiments demonstrate that, although visually more appealing, morphs based on StyleGAN 2 do not pose a significant threat to the state to face recognition systems, as these morphs were outmatched by the simple morphs that are based facial landmarks.
8 citations
20 Jun 2021
TL;DR: Zhang et al. as mentioned in this paper proposed a novel lightweight pseudo facial generation method to relieve the problem of extreme poses without generating any frontal facial image, which can depict the facial contour information and make appropriate modifications to preserve the critical identity information.
Abstract: Face recognition has achieved a great success in recent years, it is still challenging to recognize those facial images with extreme poses. Traditional methods consider it as a domain gap problem. Many of them settle it by generating fake frontal faces from extreme ones, whereas they are tough to maintain the identity information with high computational consumption and uncontrolled disturbances. Our experimental analysis shows a dramatic precision drop with extreme poses. Meanwhile, those extreme poses just exist minor visual differences after small rotations. Derived from this insight, we attempt to relieve such a huge precision drop by making minor changes to the input images without modifying existing discriminators. A novel lightweight pseudo facial generation is proposed to relieve the problem of extreme poses without generating any frontal facial image. It can depict the facial contour information and make appropriate modifications to preserve the critical identity information. Specifically, the proposed method reconstructs pseudo profile faces by minimizing the pixel-wise differences with original profile faces and maintaining the identity consistent information from their corresponding frontal faces simultaneously. The proposed framework can improve existing discriminators and obtain a great promotion on several benchmark datasets.
8 citations
TL;DR: Zhang et al. as discussed by the authors proposed the race adaptive margin based face recognition (RamFace) model, which designed under the multi-task learning framework with the race classification as the auxiliary task.
Abstract: Recent studies show that there exist significant racial bias among state-of-the-art (SOTA) face recognition algorithms, i.e., the accuracy for Caucasian is consistently higher than that for other races like African and Asian. To mitigate racial bias, we propose the race adaptive margin based face recognition (RamFace) model, designed under the multi-task learning framework with the race classification as the auxiliary task. The experiments show that the race classification task can enforce the model to learn the racial features and thus improve the discriminability of the extracted feature representations. In addition, a racial bias robust loss function, i.e., race adaptive margin loss, is proposed such that different optimal margins can be automatically derived for different races in training the model, which further mitigates the racial bias. The experimental results show that on RFW dataset, our model not only achieves SOTA face recognition accuracy but also mitigates the racial bias problem. Besides, RamFace is also tested on several public face recognition evaluation benchmarks, i.e., LFW, CPLFW and CALFW, and achieves better performance than the commonly used face recognition methods, which justifies the generalization capability of RamFace.
8 citations
TL;DR: This is the first demonstration of the feasibility of deploying camera recording systems and developing machine learning-based workflow analysis solutions for open surgery, particularly in orthopaedics.
Abstract: Safe and efficient surgical training and workflow management play a critical role in clinical competency and ultimately, patient outcomes. Video data in minimally invasive surgery (MIS) have enable...
8 citations
Cites background from "ArcFace: Additive Angular Margin Lo..."
...2019); however, even low-resolution faces can still be recognised when video sequences are available (Deng et al. 2019)....
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...Solutions to anonymise OR video include reducing image resolutions severely to preserve privacy (Srivastav et al. 2019); however, even low-resolution faces can still be recognised when video sequences are available (Deng et al. 2019)....
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TL;DR: Zhang et al. as discussed by the authors proposed a discriminative speaker embedding based on convolutional neural networks (DCNNs), which decomposes the high-dimensional frame-level features into a group of low-dimensional vectors before applying VLAD aggregation, and a mutually complementary assembling loss function is proposed to train the model, which consists of a prototypical loss and a marginal-based softmax loss.
Abstract: The embedding-based speaker verification (SV) technology has witnessed significant progress due to the advances of deep convolutional neural networks (DCNN). However, how to improve the discrimination of speaker embedding in the open world SV task is still the focus of current research in the community. In this paper, we improve the discriminative power of speaker embedding from three-fold: (1) NeXtVLAD is introduced to aggregate frame-level features, which decomposes the high-dimensional frame-level features into a group of low-dimensional vectors before applying VLAD aggregation. (2) A multi-scale aggregation strategy (MSA) assembled with NeXtVLAD is designed with the purpose of fully extract speaker information from the frame-level feature in different hidden layers of DCNN. (3) A mutually complementary assembling loss function is proposed to train the model, which consists of a prototypical loss and a marginal-based softmax loss. Extensive experiments have been conducted on the VoxCeleb-1 dataset, and the experimental results show that our proposed system can obtain significant performance improvements compared with the baseline, and obtains new state-of-the-art results. The source code of this paper is available at https://github.com/LCF2764/Discriminative-Speaker-Embedding.
8 citations
References
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28 Oct 2017
TL;DR: An automatic differentiation module of PyTorch is described — a library designed to enable rapid research on machine learning models that focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead.
Abstract: In this article, we describe an automatic differentiation module of PyTorch — a library designed to enable rapid research on machine learning models. It builds upon a few projects, most notably Lua Torch, Chainer, and HIPS Autograd [4], and provides a high performance environment with easy access to automatic differentiation of models executed on different devices (CPU and GPU). To make prototyping easier, PyTorch does not follow the symbolic approach used in many other deep learning frameworks, but focuses on differentiation of purely imperative programs, with a focus on extensibility and low overhead. Note that this preprint is a draft of certain sections from an upcoming paper covering all PyTorch features.
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Posted Content•
TL;DR: The TensorFlow interface and an implementation of that interface that is built at Google are described, which has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields.
Abstract: TensorFlow is an interface for expressing machine learning algorithms, and an
implementation for executing such algorithms. A computation expressed using
TensorFlow can be executed with little or no change on a wide variety of
heterogeneous systems, ranging from mobile devices such as phones and tablets
up to large-scale distributed systems of hundreds of machines and thousands of
computational devices such as GPU cards. The system is flexible and can be used
to express a wide variety of algorithms, including training and inference
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research and for deploying machine learning systems into production across more
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reference implementation were released as an open-source package under the
Apache 2.0 license in November, 2015 and are available at www.tensorflow.org.
10,447 citations