由于CNNS中的快速进步，因此，面部识别的性能已饱和，例如LFW，CFP-FP和AgedB，因为CNNS的快速进步。然而，由于没有这种数据集，尚未研究在FR模型上对FR模型进行各种细粒度条件的影响。本文在使用K-Face的不同条件和损耗功能方面分析了它们的效果，最近引入了具有细粒度的FR DataSet。我们提出了一种新的丢失功能，混合表面，结合了分类和度量损失。在各种基准数据集上实验证明了在有效性和稳健性方面的混合表面的优越性。

This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. However, few existing algorithms can effectively achieve this criterion. To this end, we propose the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features. Geometrically, A-Softmax loss can be viewed as imposing discriminative constraints on a hypersphere manifold, which intrinsically matches the prior that faces also lie on a manifold. Moreover, the size of angular margin can be quantitatively adjusted by a parameter m. We further derive specific m to approximate the ideal feature criterion. Extensive analysis and experiments on Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace Challenge show the superiority of A-Softmax loss in FR tasks. The code has also been made publicly available 1 .

基于软马克斯的损失函数及其变体（例如，界面，圆顶和弧形）可显着改善野生无约束场景中的面部识别性能。这些算法的一种常见实践是对嵌入特征和线性转换矩阵之间的乘法进行优化。但是，在大多数情况下，基于传统的设计经验给出了嵌入功能的尺寸，并且在给出固定尺寸时，使用该功能本身提高性能的研究较少。为了应对这一挑战，本文提出了一种称为subface的软关系近似方法，该方法采用了子空间功能来促进面部识别的性能。具体而言，我们在训练过程中动态选择每个批次中的非重叠子空间特征，然后使用子空间特征在基于软磁性的损失之间近似完整功能，因此，深层模型的可区分性可以显着增强，以增强面部识别。在基准数据集上进行的综合实验表明，我们的方法可以显着提高香草CNN基线的性能，这强烈证明了基于利润率的损失的子空间策略的有效性。

在过去的几十年中，面部识别（FR）在计算机视觉和模式识别社会中进行了积极研究。最近，由于深度学习的进步，FR技术在大多数基准数据集中都显示出高性能。但是，当将FR算法应用于现实世界的情况时，该性能仍然不令人满意。这主要归因于训练和测试集之间的不匹配。在此类不匹配中，训练和测试面之间的面部不对对准是阻碍成功的FR的因素之一。为了解决这一限制，我们提出了一个脸型引导的深度特征对齐框架，以使fr稳健地对脸错位。基于面部形状的先验（例如，面部关键点），我们通过引入对齐方式和未对准的面部图像之间的对齐过程，即像素和特征对齐方式来训练所提出的深网。通过像从面部图像和面部形状提取的聚合特征解码的像素对齐过程，我们添加了辅助任务以重建良好的面部图像。由于汇总功能通过特征对齐过程链接到面部功能提取网络作为指南，因此我们将强大的面部功能训练到面部未对准。即使在训练阶段需要面部形状估计，通常在传统的FR管道中纳入的额外面部对齐过程在测试阶段不一定需要。通过比较实验，我们验证了提出的方法与FR数据集的面部未对准的有效性。

Face recognition has made extraordinary progress owing to the advancement of deep convolutional neural networks (CNNs). The central task of face recognition, including face verification and identification, involves face feature discrimination. However, the traditional softmax loss of deep CNNs usually lacks the power of discrimination. To address this problem, recently several loss functions such as center loss, large margin softmax loss, and angular softmax loss have been proposed. All these improved losses share the same idea: maximizing inter-class variance and minimizing intra-class variance. In this paper, we propose a novel loss function, namely large margin cosine loss (LMCL), to realize this idea from a different perspective. More specifically, we reformulate the softmax loss as a cosine loss by L 2 normalizing both features and weight vectors to remove radial variations, based on which a cosine margin term is introduced to further maximize the decision margin in the angular space. As a result, minimum intra-class variance and maximum inter-class variance are achieved by virtue of normalization and cosine decision margin maximization. We refer to our model trained with LMCL as CosFace. Extensive experimental evaluations are conducted on the most popular public-domain face recognition datasets such as MegaFace Challenge, Youtube Faces (YTF) and Labeled Face in the Wild (LFW). We achieve the state-of-the-art performance on these benchmarks, which confirms the effectiveness of our proposed approach.

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023.

在本文中，我们试图在抽象嵌入空间中绘制额叶和轮廓面图像之间的连接。我们使用耦合编码器网络利用此连接将额叶/配置文件的面部图像投影到一个常见的潜在嵌入空间中。提出的模型通过最大化面部两种视图之间的相互信息来迫使嵌入空间中表示的相似性。拟议的耦合编码器从三个贡献中受益于与极端姿势差异的匹配面。首先，我们利用我们的姿势意识到的对比学习来最大程度地提高身份额叶和概况表示之间的相互信息。其次，由在过去的迭代中积累的潜在表示组成的内存缓冲区已集成到模型中，因此它可以比小批量大小相对较多的实例。第三，一种新颖的姿势感知的对抗结构域适应方法迫使模型学习从轮廓到额叶表示的不对称映射。在我们的框架中，耦合编码器学会了扩大真实面孔和冒名顶替面部分布之间的边距，这导致了相同身份的不同观点之间的高度相互信息。通过对四个基准数据集的广泛实验，评估和消融研究来研究拟议模型的有效性，并与引人入胜的最新算法进行比较。

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains K sub-centers and training samples only need to be close to any of the K positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.

In this paper, we propose a conceptually simple and geometrically interpretable objective function, i.e. additive margin Softmax (AM-Softmax), for deep face verification. In general, the face verification task can be viewed as a metric learning problem, so learning large-margin face features whose intra-class variation is small and inter-class difference is large is of great importance in order to achieve good performance. Recently, Large-margin Softmax [10] and Angular Softmax [9] have been proposed to incorporate the angular margin in a multiplicative manner. In this work, we introduce a novel additive angular margin for the Softmax loss, which is intuitively appealing and more interpretable than the existing works. We also emphasize and discuss the importance of feature normalization in the paper. Most importantly, our experiments on LFW and MegaFace show that our additive margin softmax loss consistently performs better than the current state-of-the-art methods using the same network architecture and training dataset. Our code has also been made available 1 .

横梁面部识别（CFR）旨在识别个体，其中比较面部图像源自不同的感测模式，例如红外与可见的。虽然CFR由于与模态差距相关的面部外观的显着变化，但CFR具有比经典的面部识别更具挑战性，但它在具有有限或挑战的照明的场景中，以及在呈现攻击的情况下，它是优越的。与卷积神经网络（CNNS）相关的人工智能最近的进展使CFR的显着性能提高了。由此激励，这项调查的贡献是三倍。我们提供CFR的概述，目标是通过首先正式化CFR然后呈现具体相关的应用来比较不同光谱中捕获的面部图像。其次，我们探索合适的谱带进行识别和讨论最近的CFR方法，重点放在神经网络上。特别是，我们提出了提取和比较异构特征以及数据集的重新访问技术。我们枚举不同光谱和相关算法的优势和局限性。最后，我们讨论了研究挑战和未来的研究线。

Deep metric learning has gained much popularity in recent years, following the success of deep learning. However, existing frameworks of deep metric learning based on contrastive loss and triplet loss often suffer from slow convergence, partially because they employ only one negative example while not interacting with the other negative classes in each update. In this paper, we propose to address this problem with a new metric learning objective called multi-class N -pair loss. The proposed objective function firstly generalizes triplet loss by allowing joint comparison among more than one negative examples -more specifically, N -1 negative examples -and secondly reduces the computational burden of evaluating deep embedding vectors via an efficient batch construction strategy using only N pairs of examples, instead of (N +1)×N . We demonstrate the superiority of our proposed loss to the triplet loss as well as other competing loss functions for a variety of tasks on several visual recognition benchmark, including fine-grained object recognition and verification, image clustering and retrieval, and face verification and identification.

In this paper, we investigate the problem of predictive confidence in face and kinship verification. Most existing face and kinship verification methods focus on accuracy performance while ignoring confidence estimation for their prediction results. However, confidence estimation is essential for modeling reliability in such high-risk tasks. To address this issue, we first introduce a novel yet simple confidence measure for face and kinship verification, which allows the verification models to transform the similarity score into a confidence score for a given face pair. We further propose a confidence-calibrated approach called angular scaling calibration (ASC). ASC is easy to implement and can be directly applied to existing face and kinship verification models without model modifications, yielding accuracy-preserving and confidence-calibrated probabilistic verification models. To the best of our knowledge, our approach is the first general confidence-calibrated solution to face and kinship verification in a modern context. We conduct extensive experiments on four widely used face and kinship verification datasets, and the results demonstrate the effectiveness of our approach.

随着近期神经网络的成功，对人脸识别取得了显着进展。然而，收集面部识别的大规模现实世界培训数据已经挑战，特别是由于标签噪音和隐私问题。同时，通常从网络图像收集现有的面部识别数据集，缺乏关于属性的详细注释（例如，姿势和表达），因此对面部识别的不同属性的影响已经很差。在本文中，我们使用合成面部图像，即Synface来解决面部识别中的上述问题。具体而言，我们首先探讨用合成和真实面部图像训练的最近最先进的人脸识别模型之间的性能差距。然后，我们分析了性能差距背后的潜在原因，例如，较差的阶级变化和合成和真实面部图像之间的域间隙。灵感来自于此，我们使用身份混合（IM）和域混合（DM）设计了SYNFACE，以减轻上述性能差距，展示了对面部识别的综合数据的巨大潜力。此外，利用可控的面部合成模型，我们可以容易地管理合成面代的不同因素，包括姿势，表达，照明，身份的数量和每个身份的样本。因此，我们还对综合性面部图像进行系统实证分析，以提供一些关于如何有效利用综合数据进行人脸识别的见解。

数据清洁，体系结构和损失功能设计是导致高性能面部识别的重要因素。以前，研究社区试图提高每个单个方面的性能，但未能在共同搜索所有三个方面的最佳设计时提出统一的解决方案。在本文中，我们首次确定这些方面彼此紧密结合。实际上，优化各个方面的设计实际上极大地限制了性能并偏向算法设计。具体而言，我们发现最佳模型体系结构或损耗函数与数据清洁紧密相结合。为了消除单一研究研究的偏见并提供对面部识别模型设计的总体理解，我们首先仔细设计了每个方面的搜索空间，然后引入了全面的搜索方法，以共同搜索最佳数据清洁，架构和损失功能设计。在我们的框架中，我们通过使用基于创新的增强学习方法来使拟议的全面搜索尽可能灵活。对百万级面部识别基准的广泛实验证明了我们新设计的搜索空间在每个方面和全面搜索的有效性。我们的表现要优于为每个研究轨道开发的专家算法。更重要的是，我们分析了我们搜索的最佳设计与单个因素的独立设计之间的差异。我们指出，强大的模型倾向于通过更困难的培训数据集和损失功能进行优化。我们的实证研究可以为未来的研究提供指导，以实现更健壮的面部识别系统。

可见光面图像匹配是跨模型识别的具有挑战性的变化。挑战在于，可见和热模式之间的较大的模态间隙和低相关性。现有方法采用图像预处理，特征提取或常见的子空间投影，它们本身是独立的问题。在本文中，我们提出了一种用于交叉模态面部识别的端到端框架。该算法的旨在从未处理的面部图像学习身份鉴别特征，并识别跨模态图像对。提出了一种新颖的单元级丢失，用于在丢弃模态信息时保留身份信息。另外，提出用于将图像对分类能力集成到网络中的跨模判位块。所提出的网络可用于提取无关的矢量表示或测试图像的匹配对分类。我们对五个独立数据库的跨型号人脸识别实验表明，该方法实现了对现有最先进的方法的显着改善。

面部图像的质量显着影响底层识别算法的性能。面部图像质量评估（FIQA）估计捕获的图像的效用在实现可靠和准确的识别性能方面。在这项工作中，我们提出了一种新的学习范式，可以在培训过程中学习内部网络观察。基于此，我们所提出的CR-FiQA使用该范例来通过预测其相对分类性来估计样品的面部图像质量。基于关于其类中心和最近的负类中心的角度空间中的训练样本特征表示来测量该分类性。我们通过实验说明了面部图像质量与样本相对分类性之间的相关性。由于此类属性仅为培训数据集可观察到，因此我们建议从培训数据集中学习此属性，并利用它来预测看不见样品的质量措施。该培训同时执行，同时通过用于面部识别模型训练的角度裕度罚款的软墨损失来优化类中心。通过对八个基准和四个面部识别模型的广泛评估实验，我们展示了我们提出的CR-FiQA在最先进（SOTA）FIQ算法上的优越性。

最先进的面部识别方法通常采用多分类管道，并采用基于SoftMax的损耗进行优化。虽然这些方法取得了巨大的成功，但基于Softmax的损失在开放式分类的角度下有其限制：训练阶段的多分类目标并没有严格匹配开放式分类测试的目标。在本文中，我们派生了一个名为全局边界Cosface的新损失（GB-Cosface）。我们的GB-COSface介绍了自适应全局边界，以确定两个面积是否属于相同的身份，使得优化目标与从开放集分类的角度与测试过程对齐。同时，由于损失配方来自于基于软MAX的损失，因此我们的GB-COSFace保留了基于软MAX的损耗的优异性能，并且证明了COSFace是拟议损失的特殊情况。我们在几何上分析并解释了所提出的GB-Cosface。多面识别基准测试的综合实验表明，所提出的GB-Cosface优于主流面部识别任务中的当前最先进的面部识别损失。与Cosface相比，我们的GB-Cosface在Tar @ Far = 1E-6，1E-5，1E-4上提高了1.58％，0.57％和0.28％的IJB-C基准。

This paper provides a pair similarity optimization viewpoint on deep feature learning, aiming to maximize the within-class similarity s p and minimize the between-class similarity s n . We find a majority of loss functions, including the triplet loss and the softmax cross-entropy loss, embed s n and s p into similarity pairs and seek to reduce (s n − s p ). Such an optimization manner is inflexible, because the penalty strength on every single similarity score is restricted to be equal. Our intuition is that if a similarity score deviates far from the optimum, it should be emphasized. To this end, we simply re-weight each similarity to highlight the less-optimized similarity scores. It results in a Circle loss, which is named due to its circular decision boundary. The Circle loss has a unified formula for two elemental deep feature learning paradigms, i.e., learning with class-level labels and pair-wise labels. Analytically, we show that the Circle loss offers a more flexible optimization approach towards a more definite convergence target, compared with the loss functions optimizing (s n − s p ). Experimentally, we demonstrate the superiority of the Circle loss on a variety of deep feature learning tasks. On face recognition, person re-identification, as well as several finegrained image retrieval datasets, the achieved performance is on par with the state of the art.

Recent methods for deep metric learning have been focusing on designing different contrastive loss functions between positive and negative pairs of samples so that the learned feature embedding is able to pull positive samples of the same class closer and push negative samples from different classes away from each other. In this work, we recognize that there is a significant semantic gap between features at the intermediate feature layer and class labels at the final output layer. To bridge this gap, we develop a contrastive Bayesian analysis to characterize and model the posterior probabilities of image labels conditioned by their features similarity in a contrastive learning setting. This contrastive Bayesian analysis leads to a new loss function for deep metric learning. To improve the generalization capability of the proposed method onto new classes, we further extend the contrastive Bayesian loss with a metric variance constraint. Our experimental results and ablation studies demonstrate that the proposed contrastive Bayesian metric learning method significantly improves the performance of deep metric learning in both supervised and pseudo-supervised scenarios, outperforming existing methods by a large margin.

学习歧视性面部特征在建立高性能面部识别模型方面发挥着重要作用。最近的最先进的面部识别解决方案，提出了一种在常用的分类损失函数，Softmax损失中纳入固定的惩罚率，通过最大限度地减少级别的变化来增加面部识别模型的辨别力并最大化级别的帧间变化。边缘惩罚Softmax损失，如arcFace和Cosface，假设可以使用固定的惩罚余量同样地学习不同身份之间的测地距。然而，这种学习目标对于具有不一致的间帧内变化的真实数据并不是现实的，这可能限制了面部识别模型的判别和概括性。在本文中，我们通过提出弹性罚款损失（弹性面）来放松固定的罚款边缘约束，这允许在推动阶级可分离性中灵活性。主要思想是利用从每个训练迭代中的正常分布中汲取的随机保证金值。这旨在提供决策边界机会，以提取和缩回，以允许灵活的类别可分离学习的空间。我们展示了在大量主流基准上使用相同的几何变换，展示了我们的弹性面损失和COSFace损失的优势。从更广泛的角度来看，我们的弹性面在九个主流基准中提出了最先进的面部识别性能。