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.

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.

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。

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.

图像回归任务，如骨矿物密度（BMD）估计和左心室喷射分数（LVEF）预测，在计算机辅助疾病评估中起重要作用。大多数深度回归方法用单一的回归损耗函数训练神经网络，如MSE或L1损耗。在本文中，我们提出了一种用于深度图像回归的第一个对比学习框架，即adacon，其包括通过新颖的自适应边缘对比损耗和回归预测分支的特征学习分支组成。我们的方法包含标签距离关系作为学习特征表示的一部分，这允许在下游回归任务中进行更好的性能。此外，它可以用作即插即用模块，以提高现有回归方法的性能。我们展示了adacon对来自X射线图像的骨矿物密度估计和来自超声心动图象的X射线图像和左心室喷射分数预测的骨矿物密度估计的有效性。 Adacon分别导致MAE在最先进的BMD估计和LVEF预测方法中相对提高3.3％和5.9％。

现代基于深度学习的系统的性能极大地取决于输入对象的质量。例如，对于模糊或损坏的输入，面部识别质量将较低。但是，在更复杂的情况下，很难预测输入质量对所得准确性的影响。我们提出了一种深度度量学习的方法，该方法允许直接估算不确定性，几乎没有额外的计算成本。开发的\ textit {scaleface}算法使用可训练的比例值，以修改嵌入式空间中的相似性。这些依赖于输入的量表值代表了对识别结果的信心的度量，从而允许估计不确定性。我们提供了有关面部识别任务的全面实验，这些实验表明与其他不确定性感知的面部识别方法相比，比例表面的表现出色。我们还将结果扩展到了文本到图像检索的任务，表明所提出的方法以显着的利润击败了竞争对手。

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.

随着对手工卫生的需求不断增长和使用的便利性，掌上识别最近具有淡淡的发展，为人识别提供了有效的解决方案。尽管已经致力于该地区的许多努力，但仍然不确定无接触棕榈污染的辨别能力，特别是对于大规模数据集。为了解决问题，在本文中，我们构建了一个大型无尺寸的棕榈纹数据集，其中包含了来自1167人的2334个棕榈手机。为了我们的最佳知识，它是有史以来最大的非接触式手掌形象基准，而是关于个人和棕榈树的数量收集。此外，我们提出了一个名为3DCPN（3D卷积棕榈识别网络）的无棕榈识别的新型深度学习框架，它利用3D卷积来动态地集成多个Gabor功能。在3DCPN中，嵌入到第一层中的新颖变体以增强曲线特征提取。通过精心设计的集合方案，然后将低级别的3D功能卷积以提取高级功能。最后在顶部，我们设置了基于地区的损失功能，以加强全局和本地描述符的辨别能力。为了展示我们方法的优越性，在我们的数据集和其他流行数据库同济和IITD上进行了广泛的实验，其中结果显示了所提出的3DCPN实现最先进的或可比性的性能。

本文介绍了分类器校准原理和实践的简介和详细概述。校准的分类器正确地量化了与其实例明智的预测相关的不确定性或信心水平。这对于关键应用，最佳决策，成本敏感的分类以及某些类型的上下文变化至关重要。校准研究具有丰富的历史，其中几十年来预测机器学习作为学术领域的诞生。然而，校准兴趣的最近增加导致了新的方法和从二进制到多种子体设置的扩展。需要考虑的选项和问题的空间很大，并导航它需要正确的概念和工具集。我们提供了主要概念和方法的介绍性材料和最新的技术细节，包括适当的评分规则和其他评估指标，可视化方法，全面陈述二进制和多字数分类的HOC校准方法，以及几个先进的话题。

在本文中，我们研究了现代神经网络的事后校准，这个问题近年来引起了很多关注。已经为任务提出了许多不同复杂性的校准方法，但是关于这些任务的表达方式尚无共识。我们专注于置信度缩放的任务，特别是在概括温度缩放的事后方法上，我们将其称为自适应温度缩放家族。我们分析了改善校准并提出可解释方法的表达功能。我们表明，当有大量数据复杂模型（例如神经网络）产生更好的性能时，但是当数据量受到限制时，很容易失败，这是某些事后校准应用（例如医学诊断）的常见情况。我们研究表达方法在理想条件和设计更简单的方法下学习但对这些表现良好的功能具有强烈的感应偏见的功能。具体而言，我们提出了基于熵的温度缩放，这是一种简单的方法，可根据其熵缩放预测的置信度。结果表明，与其他方法相比，我们的方法可获得最先进的性能，并且与复杂模型不同，它对数据稀缺是可靠的。此外，我们提出的模型可以更深入地解释校准过程。

Recent studies have revealed that, beyond conventional accuracy, calibration should also be considered for training modern deep neural networks. To address miscalibration during learning, some methods have explored different penalty functions as part of the learning objective, alongside a standard classification loss, with a hyper-parameter controlling the relative contribution of each term. Nevertheless, these methods share two major drawbacks: 1) the scalar balancing weight is the same for all classes, hindering the ability to address different intrinsic difficulties or imbalance among classes; and 2) the balancing weight is usually fixed without an adaptive strategy, which may prevent from reaching the best compromise between accuracy and calibration, and requires hyper-parameter search for each application. We propose Class Adaptive Label Smoothing (CALS) for calibrating deep networks, which allows to learn class-wise multipliers during training, yielding a powerful alternative to common label smoothing penalties. Our method builds on a general Augmented Lagrangian approach, a well-established technique in constrained optimization, but we introduce several modifications to tailor it for large-scale, class-adaptive training. Comprehensive evaluation and multiple comparisons on a variety of benchmarks, including standard and long-tailed image classification, semantic segmentation, and text classification, demonstrate the superiority of the proposed method. The code is available at https://github.com/by-liu/CALS.

使用面部作为生物识别标识特征是通过捕获过程的非接触性质和识别算法的高准确度的激励。在目前的Covid-19大流行之后，在公共场所施加了面膜，以保持大流行。然而，由于戴着面具而面的遮挡是面部识别系统的新出现挑战。在本文中，我们提出了一种改进掩蔽面部识别性能的解决方案。具体地，我们提出了在现有面部识别模型的顶部操作的嵌入揭露模型（EUM）。我们还提出了一种新颖的损失功能，自限制的三态（SRT），使欧莱斯能够产生类似于相同身份的未掩蔽面的嵌入物。实现了三个面部识别模型，两个真实屏蔽数据集和两个合成产生的掩蔽面部数据集所取得的评价结果​​证明我们的提出方法在大多数实验环境中显着提高了性能。

面部识别系统必须处理可能导致匹配决策不正确的大型变量（例如不同的姿势，照明和表达）。这些可变性可以根据面部图像质量来测量，这在样本的效用上定义了用于识别的实用性。以前的识别作品不使用这种有价值的信息或利用非本质上的质量估算。在这项工作中，我们提出了一种简单且有效的面部识别解决方案（Qmagface），其将质量感知的比较分数与基于大小感知角裕度损耗的识别模型相结合。所提出的方法包括比较过程中特定于模型的面部图像质量，以增强在无约束情况下的识别性能。利用利用损失诱导的质量与其比较评分之间的线性，我们的质量意识比较功能简单且高度普遍。在几个面部识别数据库和基准上进行的实验表明，引入的质量意识导致识别性能一致的改进。此外，所提出的Qmagface方法在挑战性环境下特别好，例如交叉姿势，跨年或跨品。因此，它导致最先进的性能在几个面部识别基准上，例如在XQLFQ上的98.50％，83.97％，CFP-FP上的98.74％。 QMagface的代码是公开可用的。

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 .

尽管深神经网络的占优势性能，但最近的作品表明它们校准不佳，导致过度自信的预测。由于培训期间的跨熵最小化，因此可以通过过度化来加剧错误烫伤，因为它促进了预测的Softmax概率来匹配单热标签分配。这产生了正确的类别的Pre-SoftMax激活，该类别明显大于剩余的激活。来自文献的最近证据表明，损失函数嵌入隐含或明确最大化的预测熵会产生最先进的校准性能。我们提供了当前最先进的校准损耗的统一约束优化视角。具体地，这些损失可以被视为在Logit距离上施加平等约束的线性惩罚（或拉格朗日）的近似值。这指出了这种潜在的平等约束的一个重要限制，其随后的梯度不断推动非信息解决方案，这可能会阻止在基于梯度的优化期间模型的辨别性能和校准之间的最佳妥协。在我们的观察之后，我们提出了一种基于不平等约束的简单灵活的泛化，这在Logit距离上强加了可控裕度。关于各种图像分类，语义分割和NLP基准的综合实验表明，我们的方法在网络校准方面对这些任务设置了新的最先进的结果，而不会影响辨别性能。代码可在https://github.com/by-liu/mbls上获得。

Data uncertainty is commonly observed in the images for face recognition (FR). However, deep learning algorithms often make predictions with high confidence even for uncertain or irrelevant inputs. Intuitively, FR algorithms can benefit from both the estimation of uncertainty and the detection of out-of-distribution (OOD) samples. Taking a probabilistic view of the current classification model, the temperature scalar is exactly the scale of uncertainty noise implicitly added in the softmax function. Meanwhile, the uncertainty of images in a dataset should follow a prior distribution. Based on the observation, a unified framework for uncertainty modeling and FR, Random Temperature Scaling (RTS), is proposed to learn a reliable FR algorithm. The benefits of RTS are two-fold. (1) In the training phase, it can adjust the learning strength of clean and noisy samples for stability and accuracy. (2) In the test phase, it can provide a score of confidence to detect uncertain, low-quality and even OOD samples, without training on extra labels. Extensive experiments on FR benchmarks demonstrate that the magnitude of variance in RTS, which serves as an OOD detection metric, is closely related to the uncertainty of the input image. RTS can achieve top performance on both the FR and OOD detection tasks. Moreover, the model trained with RTS can perform robustly on datasets with noise. The proposed module is light-weight and only adds negligible computation cost to the model.

自动识别面部和声音的明显情绪很难，部分原因是各种不确定性来源，包括输入数据和机器学习框架中使用的标签。本文介绍了一种不确定性感知的视听融合方法，该方法量化了对情绪预测的模态不确定性。为此，我们提出了一个新颖的融合框架，在该框架中，我们首先通过视听时间上下文向量学习潜在分布，然后限制单峰潜在分布的方差向量，以便它们表示每种模式的信息量，以提供W.R.T.情绪识别。特别是，我们对视听潜在分布的方差向量施加了校准和序数排名约束。当经过良好校准时，将模态不确定性得分表明它们的相应预测可能与地面真实标签有多大不同。排名良好的不确定性得分允许在模式中对不同框架进行顺序排名。为了共同施加这两种约束，我们提出了软马克斯分布匹配损失。在分类和回归设置中，我们将不确定性感知的融合模型与标准模型 - 静态融合基线进行了比较。我们对两个情绪识别语料库（AVEC 2019 CES和IEMOCAP）的评估表明，视听情绪识别可以从良好的和良好的潜在不确定性度量中受益匪浅。

Confidence calibration -the problem of predicting probability estimates representative of the true correctness likelihood -is important for classification models in many applications. We discover that modern neural networks, unlike those from a decade ago, are poorly calibrated. Through extensive experiments, we observe that depth, width, weight decay, and Batch Normalization are important factors influencing calibration. We evaluate the performance of various post-processing calibration methods on state-ofthe-art architectures with image and document classification datasets. Our analysis and experiments not only offer insights into neural network learning, but also provide a simple and straightforward recipe for practical settings: on most datasets, temperature scaling -a singleparameter variant of Platt Scaling -is surprisingly effective at calibrating predictions.

随着最近深度卷积神经网络的进步，一般面临的概念取得了重大进展。然而，最先进的一般面部识别模型对遮挡面部图像没有概括，这正是现实世界场景中的常见情况。潜在原因是用于训练和特定设计的大规模遮挡面部数据，用于解决闭塞所带来的损坏功能。本文提出了一种新颖的面部识别方法，其基于单端到端的深神经网络的闭塞是强大的。我们的方法（使用遮挡掩码）命名（面部识别），学会发现深度卷积神经网络的损坏功能，并通过动态学习的面具清洁它们。此外，我们构建了大规模的遮挡面部图像，从有效且有效地培训。与现有方法相比，依靠外部探测器发现遮挡或采用较少鉴别的浅模型的现有方法，从简单且功能强大。 LFW，Megaface挑战1，RMF2，AR数据集和其他模拟遮挡/掩蔽数据集的实验结果证实，从大幅提高了遮挡下的准确性，并概括了一般面部识别。

位置识别是同时定位和映射（SLAM）和空间感知的关键。但是，野外的地方识别通常会因图像变化（例如改变观点和街头外观）而产生错误的预测。将不确定性估计纳入地点识别的生命周期是减轻变化对位置识别性能的影响的有前途的方法。但是，这种静脉的现有不确定性估计方法要么是计算效率低下（例如蒙特卡洛辍学），要么以降低准确性为代价。本文提出了Stun，这是一个自学框架，该框架学会同时预测位置并估计给定输入图像的预测不确定性。为此，我们首先使用标准的度量学习管道训练老师网培训网络，以生产嵌入培训。然后，在经过预告片的教师网络监督的情况下，培训了一个具有额外差异分支的学生网，以对嵌入先验的培训进行训练，并按样本估算不确定性样本。在在线推理阶段，我们仅使用学生网与不确定性结合产生位置预测。与对不确定性一无所知的位置识别系统相比，我们的框架具有自由估计的不确定性估计而无需牺牲任何预测准确性。我们对大规模匹兹堡30K数据集的实验结果表明，昏迷在识别精度和不确定性估计质量方面的表现都优于最先进的方法。