Recent face recognition experiments on a major benchmark (LFW [14]) show stunning performance-a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms with increasing numbers of "distractors" (going from 10 to 1M) in the gallery set. We present both identification and verification performance, evaluate performance with respect to pose and a persons age, and compare as a function of training data size (#photos and #people). We report results of state of the art and baseline algorithms. The MegaFace dataset, baseline code, and evaluation scripts, are all publicly released for further experimentations 1 .

Most face databases have been created under controlled conditions to facilitate the study of specific parameters on the face recognition problem. These parameters include such variables as position, pose, lighting, background, camera quality, and gender. While there are many applications for face recognition technology in which one can control the parameters of image acquisition, there are also many applications in which the practitioner has little or no control over such parameters. This database, Labeled Faces in the Wild, is provided as an aid in studying the latter, unconstrained, recognition problem. The database contains labeled face photographs spanning the range of conditions typically encountered in everyday life. The database exhibits "natural" variability in factors such as pose, lighting, race, accessories, occlusions, and background. In addition to describing the details of the database, we provide specific experimental paradigms for which the database is suitable. This is done in an effort to make research performed with the database as consistent and comparable as possible. We provide baseline results, including results of a state of the art face recognition system combined with a face alignment system. To facilitate experimentation on the database, we provide several parallel databases, including an aligned version.

自动面部识别是一个知名的研究领域。在该领域的最后三十年的深入研究中，已经提出了许多不同的面部识别算法。随着深度学习的普及及其解决各种不同问题的能力，面部识别研究人员集中精力在此范式下创建更好的模型。从2015年开始，最先进的面部识别就植根于深度学习模型。尽管有大规模和多样化的数据集可用于评估面部识别算法的性能，但许多现代数据集仅结合了影响面部识别的不同因素，例如面部姿势，遮挡，照明，面部表情和图像质量。当算法在这些数据集上产生错误时，尚不清楚哪些因素导致了此错误，因此，没有指导需要多个方向进行更多的研究。这项工作是我们以前在2014年开发的作品的后续作品，最终于2016年发表，显示了各种面部方面对面部识别算法的影响。通过将当前的最新技术与过去的最佳系统进行比较，我们证明了在强烈的遮挡下，某些类型的照明和强烈表达的面孔是深入学习算法所掌握的问题，而具有低分辨率图像的识别，极端的姿势变化和开放式识别仍然是一个开放的问题。为了证明这一点，我们使用六个不同的数据集和五种不同的面部识别算法以开源和可重现的方式运行一系列实验。我们提供了运行所有实验的源代码，这很容易扩展，因此在我们的评估中利用自己的深网只有几分钟的路程。

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians).The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimise the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity.To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VG-GFace2, on MS-Celeb-1M, and on their union, and show that training on VGGFace2 leads to improved recognition performance over pose and age. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on the face recognition of IJB datasets, exceeding the previous state-of-the-art by a large margin. The dataset and models are publicly available 1 .

很少有研究重点是研究人们如何识别变形攻击，即使有一些出版物已经检查了自动化FRS的敏感性并提供了变形攻击检测（MAD）方法。 MAD接近他们的决策要么基于单个图像，因此没有参考以比较（S-MAD）或使用参考图像（D-MAD）。一个普遍的误解是，审查员或观察者的面部变体检测能力取决于他们的主题专业知识，经验和对这个问题的熟悉程度，并且没有任何作品报告了定期验证身份（ID）文档的观察者的具体结果。当人类观察者参与检查具有面部图像的ID文件时，其能力的失误可能会面临重大的社会挑战。为了评估观察者的熟练程度，这项工作首先构建了来自48位不同受试者的现实变形攻击的新基准数据库，从而产生了400个变形图像。我们还捕获了从自动边界控制（ABC）门的图像，以模仿D-MAD设置中现实的边界横断场景，并使用400个探针图像研究人类观察者检测变形图像的能力。还生产了一个新的180个变形图像的数据集，以研究S-MAD环境中的人类能力。除了创建一个新的评估平台来进行S-MAD和D-MAD分析外，该研究还雇用了469位D-MAD的观察员，S-MAD的410位观察员和410位观察员，他们主要是来自40多个国家 /地区的政府雇员，以及103个科目谁不是考官。该分析提供了有趣的见解，并突出了缺乏专业知识和未能认识到专家大量变形攻击的缺乏。这项研究的结果旨在帮助制定培训计划，以防止安全失败，同时确定图像是真正的还是改变了图像。

Facial recognition is fundamental for a wide variety of security systems operating in real-time applications. In video surveillance based face recognition, face images are typically captured over multiple frames in uncontrolled conditions; where head pose, illumination, shadowing, motion blur and focus change over the sequence. We can generalize that the three fundamental operations involved in the facial recognition tasks: face detection, face alignment and face recognition. This study presents comparative benchmark tables for the state-of-art face recognition methods by testing them with same backbone architecture in order to focus only on the face recognition solution instead of network architecture. For this purpose, we constructed a video surveillance dataset of face IDs that has high age variance, intra-class variance (face make-up, beard, etc.) with native surveillance facial imagery data for evaluation. On the other hand, this work discovers the best recognition methods for different conditions like non-masked faces, masked faces, and faces with glasses.

随着面部生物识别技术的广泛采用，在自动面部识别（FR）应用中区分相同的双胞胎和非双胞胎外观相似的问题变得越来越重要。由于同卵双胞胎和外观相似的面部相似性很高，因此这些面对对面部识别工具表示最困难的病例。这项工作介绍了迄今为止汇编的最大的双胞胎数据集之一，以应对两个挑战：1）确定相同双胞胎和2）的面部相似性的基线度量和2）应用此相似性措施来确定多ppelgangers的影响或外观 - Alikes，关于大面部数据集的FR性能。面部相似性度量是通过深度卷积神经网络确定的。该网络经过量身定制的验证任务进行培训，旨在鼓励网络在嵌入空间中将高度相似的面对对组合在一起，并达到0.9799的测试AUC。所提出的网络为任何两个给定的面提供了定量相似性评分，并已应用于大规模面部数据集以识别相似的面对对。还执行了一个附加分析，该分析还将面部识别工具返回的比较分数以及提议网络返回的相似性分数。

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.

Recent studies demonstrate that machine learning algorithms can discriminate based on classes like race and gender. In this work, we present an approach to evaluate bias present in automated facial analysis algorithms and datasets with respect to phenotypic subgroups. Using the dermatologist approved Fitzpatrick Skin Type classification system, we characterize the gender and skin type distribution of two facial analysis benchmarks, IJB-A and Adience. We find that these datasets are overwhelmingly composed of lighter-skinned subjects (79.6% for IJB-A and 86.2% for Adience) and introduce a new facial analysis dataset which is balanced by gender and skin type. We evaluate 3 commercial gender classification systems using our dataset and show that darker-skinned females are the most misclassified group (with error rates of up to 34.7%). The maximum error rate for lighter-skinned males is 0.8%. The substantial disparities in the accuracy of classifying darker females, lighter females, darker males, and lighter males in gender classification systems require urgent attention if commercial companies are to build genuinely fair, transparent and accountable facial analysis algorithms.

深度卷积神经网络（DCNNS）在面部识别方面已经达到了人类水平的准确性（Phillips等，2018），尽管目前尚不清楚它们如何准确地区分高度相似的面孔。在这里，人类和DCNN执行了包括相同双胞胎在内的具有挑战性的面貌匹配任务。参与者（n = 87）查看了三种类型的面孔图像：同一身份，普通冒名顶替对（来自相似人口组的不同身份）和双胞胎冒名顶替对（相同的双胞胎兄弟姐妹）。任务是确定对是同一个人还是不同的人。身份比较在三个观点区分条件下进行了测试：额叶至额叶，额叶至45度，额叶为90度。在每个观点 - 差异条件下评估了从双胞胎突变器和一般冒险者区分匹配的身份对的准确性。人类对于一般撞击对比双重射手对更准确，准确性下降，一对图像之间的观点差异增加。通过介绍给人类的同一图像对测试了经过训练的面部识别的DCNN（Ranjan等，2018）。机器性能反映了人类准确性的模式，但除了一种条件以外，所有人的性能都处于或尤其是所有人的表现。在所有图像对类型中，比较了人与机器的相似性得分。该项目级别的分析表明，在九种图像对类型中的六种中，人类和机器的相似性等级显着相关[范围r = 0.38至r = 0.63]，这表明人类对面部相似性的感知和DCNN之间的一般协议。这些发现还有助于我们理解DCNN的表现，以区分高度介绍面孔，表明DCNN在人类或以上的水平上表现出色，并暗示了人类和DCNN使用的特征之间的均等程度。

媒体报道指责人们对“偏见”'，“”性别歧视“和”种族主义“的人士指责。研究文献中有共识，面部识别准确性为女性较低，妇女通常具有更高的假匹配率和更高的假非匹配率。然而，几乎没有出版的研究，旨在识别女性准确性较低的原因。例如，2019年的面部识别供应商测试将在广泛的算法和数据集中记录较低的女性准确性，并且数据集也列出了“分析原因和效果”在“我们没有做的东西”下''。我们介绍了第一个实验分析，以确定在去以前研究的数据集上对女性的较低人脸识别准确性的主要原因。在测试图像中控制相等的可见面部可见面积减轻了女性的表观更高的假非匹配率。其他分析表明，化妆平衡数据集进一步改善了女性以实现较低的虚假非匹配率。最后，聚类实验表明，两种不同女性的图像本质上比两种不同的男性更相似，潜在地占错误匹配速率的差异。

这项工作总结了2022年2022年国际生物识别联合会议（IJCB 2022）的IJCB被遮挡的面部识别竞赛（IJCB-OCFR-2022）。OCFR-2022从学术界吸引了总共3支参与的团队。最终，提交了六个有效的意见书，然后由组织者评估。在严重的面部阻塞面前，举行了竞争是为了应对面部识别的挑战。参与者可以自由使用任何培训数据，并且通过使用众所周知的数据集构成面部图像的部分来构建测试数据。提交的解决方案提出了创新，并以所考虑的基线表现出色。这项竞争的主要输出是具有挑战性，现实，多样化且公开可用的遮挡面部识别基准，并具有明确的评估协议。

脸部是人类识别最广泛使用的特征之一，即使在许多大规模的应用中也是如此。尽管在面部识别系统中推进技术进步，但它们仍然面临由姿势，表达，闭塞和老化变化引起的障碍。由于Covid-19大流行，非接触式身份验证变得非常重要。要限制大流行，人们已经开始使用面膜。最近，已经对面膜对成人面部识别系统的影响进行了少数研究。然而，老化与面部面膜对儿童主体识别的影响尚未得到充分的探索。因此，本研究的主要目的是与面罩和面罩和面部识别系统的其他协变量分析儿童纵向冲击。具体而言，我们在儿童串行验证和识别设置下对三个顶级执行公共面部匹配器和COVID-19商业现成（COTS）系统的比较调查，使用我们所产生的合成面具和识别设置。面具样品。此外，我们调查了眼镜与掩模和无面具的纵向后果。该研究利用无面罩纵向儿童数据集（即扩展的印度儿童纵向面部数据集），其中包含$ 26,258 $面部图像的$ [2,18] $ 3.35 $的平均时间跨度年。实验结果表明，自动面部识别面膜的问题通过老化变化复合。

面部检测是计算机愿景领域的长期挑战，最终目标是准确地将人类面临着不受约束的环境。由于与姿势，图像分辨率，照明，闭塞和观点相关的混淆因素，使这些系统具有重要的技术障碍。据说，随着最近的机器学习的发展，面部检测系统实现了非凡的准确性，主要是基于数据驱动的深度学习模型[70]。虽然鼓励，限制了部署系统的面部检测性能和社会责任的关键方面是人类外观的固有多样性。每个人类的外表都反映了一个人的东西，包括他们的遗产，身份，经验和自我表达的可见表现。但是，有关面部检测系统如何在面对不同的面部尺寸和形状，肤色，身体修改和身体装饰方面进行良好的表现问题。为了实现这一目标，我们收集了独特的人类外观数据集，这是一种图像集，表示具有低频率的外观，并且往往是面部数据集的缺点。然后，我们评估了当前最先进的脸部检测模型，其能够检测这些图像中的面部。评估结果表明，面部检测算法对这些不同的外观没有概括。评估和表征当前的面部检测模型的状态将加速研究和开发，以创造更公平和更准确的面部检测系统。

Biometrics are one of the most privacy-sensitive data. Ubiquitous authentication systems with a focus on privacy favor decentralized approaches as they reduce potential attack vectors, both on a technical and organizational level. The gold standard is to let the user be in control of where their own data is stored, which consequently leads to a high variety of devices used. Moreover, in comparison with a centralized system, designs with higher end-user freedom often incur additional network overhead. Therefore, when using face recognition for biometric authentication, an efficient way to compare faces is important in practical deployments, because it reduces both network and hardware requirements that are essential to encourage device diversity. This paper proposes an efficient way to aggregate embeddings used for face recognition based on an extensive analysis on different datasets and the use of different aggregation strategies. As part of this analysis, a new dataset has been collected, which is available for research purposes. Our proposed method supports the construction of massively scalable, decentralized face recognition systems with a focus on both privacy and long-term usability.

基于全面的生物识别是一个广泛的研究区域。然而，仅使用部分可见的面，例如在遮盖的人的情况下，是一个具有挑战性的任务。在这项工作中使用深卷积神经网络（CNN）来提取来自遮盖者面部图像的特征。我们发现，第六和第七完全连接的层，FC6和FC7分别在VGG19网络的结构中提供了鲁棒特征，其中这两层包含4096个功能。这项工作的主要目标是测试基于深度学习的自动化计算机系统的能力，不仅要识别人，还要对眼睛微笑等性别，年龄和面部表达的认可。我们的实验结果表明，我们为所有任务获得了高精度。最佳记录的准确度值高达99.95％，用于识别人员，99.9％，年龄识别的99.9％，面部表情（眼睛微笑）认可为80.9％。

广泛认为，面部识别准确性存在“性别差距”，女性具有较高的错误匹配和错误的非匹配率。但是，关于这种性别差距的原因，相对较少了解。甚至最近有关人口影响的NIST报告也列出了“我们没有做的事情”下的“分析因果”。我们首先证明女性和男性发型具有影响面部识别准确性的重要差异。特别是，与女性相比，男性面部毛发有助于在不同男性面孔之间产生更大的外观平均差异。然后，我们证明，当用来估计识别精度的数据在性别之间保持平衡，以使发型如何阻塞面部时，最初观察到的性别差距在准确性上大大消失。我们为两个不同的匹配者展示了这一结果，并分析了白种人和非裔美国人的图像。这些结果表明，对准确性的人口统计学差异的未来研究应包括检查测试数据的平衡质量，作为问题制定的一部分。为了促进可重复的研究，将公开使用此研究中使用的匹配项，属性分类器和数据集。

In modern face recognition, the conventional pipeline consists of four stages: detect ⇒ align ⇒ represent ⇒ classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4,000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

Person recognition at a distance entails recognizing the identity of an individual appearing in images or videos collected by long-range imaging systems such as drones or surveillance cameras. Despite recent advances in deep convolutional neural networks (DCNNs), this remains challenging. Images or videos collected by long-range cameras often suffer from atmospheric turbulence, blur, low-resolution, unconstrained poses, and poor illumination. In this paper, we provide a brief survey of recent advances in person recognition at a distance. In particular, we review recent work in multi-spectral face verification, person re-identification, and gait-based analysis techniques. Furthermore, we discuss the merits and drawbacks of existing approaches and identify important, yet under explored challenges for deploying remote person recognition systems in-the-wild.

深度神经网络在人类分析中已经普遍存在，增强了应用的性能，例如生物识别识别，动作识别以及人重新识别。但是，此类网络的性能通过可用的培训数据缩放。在人类分析中，对大规模数据集的需求构成了严重的挑战，因为数据收集乏味，廉价，昂贵，并且必须遵守数据保护法。当前的研究研究了\ textit {合成数据}的生成，作为在现场收集真实数据的有效且具有隐私性的替代方案。这项调查介绍了基本定义和方法，在生成和采用合成数据进行人类分析时必不可少。我们进行了一项调查，总结了当前的最新方法以及使用合成数据的主要好处。我们还提供了公开可用的合成数据集和生成模型的概述。最后，我们讨论了该领域的局限性以及开放研究问题。这项调查旨在为人类分析领域的研究人员和从业人员提供。