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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01 Oct 2019
TL;DR: This work introduces the first hybrid loss function that jointly performs classification and clustering in a single formulation based on an `affinity measure' in Euclidean space that leads to the following benefits: direct enforcement of maximum margin constraints on classification boundaries and flexibility to learn multiple class prototypes to support diversity and discriminability in feature space.
Abstract: Real-world object classes appear in imbalanced ratios. This poses a significant challenge for classifiers which get biased towards frequent classes. We hypothesize that improving the generalization capability of a classifier should improve learning on imbalanced datasets. Here, we introduce the first hybrid loss function that jointly performs classification and clustering in a single formulation. Our approach is based on an `affinity measure' in Euclidean space that leads to the following benefits: (1) direct enforcement of maximum margin constraints on classification boundaries, (2) a tractable way to ensure uniformly spaced and equidistant cluster centers, (3) flexibility to learn multiple class prototypes to support diversity and discriminability in feature space. Our extensive experiments demonstrate the significant performance improvements on visual classification and verification tasks on multiple imbalanced datasets. The proposed loss can easily be plugged in any deep architecture as a differentiable block and demonstrates robustness against different levels of data imbalance and corrupted labels.
70 citations
Cites background from "ArcFace: Additive Angular Margin Lo..."
...Among these, Large-margin softmax [34] enforces inter-class separability directly on the dot-product similarity while SphereFace [33] and ArcFace [10] enforce multiplicative and additive angular margins on the hypersphere manifold, respectively....
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...Enforcing margin between classes: Note that some variants of soft-max loss introduce angle based margin constraints [33, 10], however, the margins in angular domain are computationally expensive and implemented only as approximations due to intractability....
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...Remarkably, some recent efforts focus on introducing max-margin constraints within the soft-max loss function [10, 34, 33]....
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14 Jun 2020
Abstract: Generalized zero-shot learning aims to recognize images from seen and unseen domains. Recent methods focus on learning a unified semantic-aligned visual representation to transfer knowledge between two domains, while ignoring the effect of semantic-free visual representation in alleviating the biased recognition problem. In this paper, we propose a novel Domain-aware Visual Bias Eliminating (DVBE) network that constructs two complementary visual representations, i.e., semantic-free and semantic-aligned, to treat seen and unseen domains separately. Specifically, we explore cross-attentive second-order visual statistics to compact the semantic-free representation, and design an adaptive margin Softmax to maximize inter-class divergences. Thus, the semantic-free representation becomes discriminative enough to not only predict seen class accurately but also filter out unseen images, i.e., domain detection, based on the predicted class entropy. For unseen images, we automatically search an optimal semantic-visual alignment architecture, rather than manual designs, to predict unseen classes. With accurate domain detection, the biased recognition problem towards the seen domain is significantly reduced. Experiments on five benchmarks for classification and segmentation show that DVBE outperforms existing methods by averaged 5.7% improvement.
69 citations
Posted Content•
TL;DR: Three different margin based losses which not only separate classes but also demand a fixed margin between classes are introduced to deep speaker embedding learning and it could be demonstrated that the margin is the key to obtain more discriminative speaker embeddings.
Abstract: Recently, speaker embeddings extracted from a speaker discriminative deep neural network (DNN) yield better performance than the conventional methods such as i-vector. In most cases, the DNN speaker classifier is trained using cross entropy loss with softmax. However, this kind of loss function does not explicitly encourage inter-class separability and intra-class compactness. As a result, the embeddings are not optimal for speaker recognition tasks. In this paper, to address this issue, three different margin based losses which not only separate classes but also demand a fixed margin between classes are introduced to deep speaker embedding learning. It could be demonstrated that the margin is the key to obtain more discriminative speaker embeddings. Experiments are conducted on two public text independent tasks: VoxCeleb1 and Speaker in The Wild (SITW). The proposed approach can achieve the state-of-the-art performance, with 25% ~ 30% equal error rate (EER) reduction on both tasks when compared to strong baselines using cross entropy loss with softmax, obtaining 2.238% EER on VoxCeleb1 test set and 2.761% EER on SITW core-core test set, respectively.
67 citations
Cites background from "ArcFace: Additive Angular Margin Lo..."
...In this work, to encourage discriminative embedding learning, three losses that impose a fixed margin between classes are studied: angular softmax loss (denoted by A-Softmax loss) [9], additive margin softmax loss (denoted by AMSoftmax loss) [10, 11] and additive angular margin loss (denoted by AAM-Softmax loss) [12]....
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14 Jun 2020
TL;DR: Zhang et al. as mentioned in this paper proposed a novel concept to measure face quality based on an arbitrary face recognition model by determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated.
Abstract: Face image quality is an important factor to enable high-performance face recognition systems. Face quality assessment aims at estimating the suitability of a face image for the purpose of recognition. Previous work proposed supervised solutions that require artificially or human labelled quality values. However, both labelling mechanisms are error prone as they do not rely on a clear definition of quality and may not know the best characteristics for the utilized face recognition system. Avoiding the use of inaccurate quality labels, we proposed a novel concept to measure face quality based on an arbitrary face recognition model. By determining the embedding variations generated from random subnetworks of a face model, the robustness of a sample representation and thus, its quality is estimated. The experiments are conducted in a cross-database evaluation setting on three publicly available databases. We compare our proposed solution on two face embeddings against six state-of-the-art approaches from academia and industry. The results show that our unsupervised solution outperforms all other approaches in the majority of the investigated scenarios. In contrast to previous works, the proposed solution shows a stable performance over all scenarios. Utilizing the deployed face recognition model for our face quality assessment methodology avoids the training phase completely and further outperforms all baseline approaches by a large margin. Our solution can be easily integrated into current face recognition systems, and can be modified to other tasks beyond face recognition.
67 citations
01 Nov 2020
TL;DR: A magnitude estimation network that is combined with a modified ResNet x-vector system to generate embeddings whose inner product is able to produce calibrated scores with increased discrimination and calibration gains at multiple operating points is presented.
Abstract: We present a magnitude estimation network that is combined with a modified ResNet x-vector system to generate embeddings whose inner product is able to produce calibrated scores with increased discrimination. A three-step training procedure is used. First, the network is trained using short segments and a multi-class cross-entropy loss with angular margin softmax. During the second step, only a reduced subset of the DNN parameters are refined using full-length recordings. Finally, the magnitude estimation network is trained using a binary crossentropy loss over pairs of target and non-target trials. The resulting system is evaluated on 4 widely-used benchmarks and provides significant discrimination and calibration gains at multiple operating points.
66 citations
Cites background from "ArcFace: Additive Angular Margin Lo..."
...A number of variants have been proposed [21, 28] to reduce the interclass variance by introducing the notion of a margin penalty to the target class logit....
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