面部美容预测(FBP)旨在开发一种机器,自动制作面部吸引力评估。在过去的情况下,结果与人类评分高度相关,因此也与注释的偏差相同。由于人工智能可以具有种族主义和歧视性倾向,必须识别数据中偏差的原因。培训数据的开发和对抗偏见信息具有强大的算法是科学家的新挑战。随着审美判断通常偏见,我们希望进一步迈出一步,并为FBP提出一个非偏见的卷积神经网络。虽然可以从道德角度创建可以对脸部的吸引力的网络模型,从道德的角度来看,它同样重要的是要确保模型是无偏的。在这项工作中,我们引入了美学,最先进的吸引力预测网络,这显着优于竞争对手0.9601的Pearson相关性。此外,我们提出了一种新的方法,用于产生无偏见的CNN,以改善机器学习中的公平性。
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在过去的几十年里,机器和深度学习界在挑战性的任务中庆祝了巨大成就,如图像分类。人工神经网络的深度建筑与可用数据的宽度一起使得可以描述高度复杂的关系。然而,仍然不可能完全捕捉深度学习模型已经了解到的深度学习模型并验证它公平,而不会产生偏见,特别是在临界任务中,例如在医学领域产生的问题。这样的任务的一个示例是检测面部图像中的不同面部表情,称为动作单位。考虑到这项特定任务,我们的研究旨在为偏见提供透明度,具体与性别和肤色有关。我们训练一个神经网络进行动作单位分类,并根据其准确性和基于热量的定性分析其性能。对我们的结果的结构化审查表明我们能够检测到偏见。尽管我们不能从我们的结果得出结论,但较低的分类表现完全来自性别和肤色偏差,这些偏差必须得到解决,这就是为什么我们通过提出关于如何避免检测到的偏差的建议。
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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。
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本文介绍了一个新颖的数据集,以帮助研究人员评估他们的计算机视觉和音频模型,以便在各种年龄,性别,表观肤色和环境照明条件下进行准确性。我们的数据集由3,011名受试者组成,并包含超过45,000个视频,平均每人15个视频。这些视频被录制在多个美国国家,各种成年人在各种年龄,性别和明显的肤色群体中。一个关键特征是每个主题同意参与他们使用的相似之处。此外,我们的年龄和性别诠释由受试者自己提供。一组训练有素的注释器使用FitzPatrick皮肤型刻度标记了受试者的表观肤色。此外,还提供了在低环境照明中记录的视频的注释。作为衡量某些属性的预测稳健性的申请,我们对DeepFake检测挑战(DFDC)的前五名获胜者提供了全面的研究。实验评估表明,获胜模型对某些特定人群的表现较小,例如肤色较深的肤色,因此可能对所有人都不概括。此外,我们还评估了最先进的明显年龄和性别分类方法。我们的实验在各种背景的人们的公平待遇方面对这些模型进行了彻底的分析。
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计算机视觉(CV)取得了显着的结果,在几个任务中表现优于人类。尽管如此,如果不正确处理,可能会导致重大歧视,因为CV系统高度依赖于他们所用的数据,并且可以在此类数据中学习和扩大偏见。因此,理解和发现偏见的问题至关重要。但是,没有关于视觉数据集中偏见的全面调查。因此,这项工作的目的是:i)描述可能在视觉数据集中表现出来的偏差; ii)回顾有关视觉数据集中偏置发现和量化方法的文献; iii)讨论现有的尝试收集偏见视觉数据集的尝试。我们研究的一个关键结论是,视觉数据集中发现和量化的问题仍然是开放的,并且在方法和可以解决的偏见范围方面都有改进的余地。此外,没有无偏见的数据集之类的东西,因此科学家和从业者必须意识到其数据集中的偏见并使它们明确。为此,我们提出了一个清单,以在Visual DataSet收集过程中发现不同类型的偏差。
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儿童性滥用和剥削(CSAE)受害者的确切年龄估计是最重要的数字取证挑战之一。调查人员通常需要通过查看图像和解释性发展阶段和其他人类特征来确定受害者的年龄。主要优先事项 - 保障儿童 - 通常受到这项工作可能需要的巨大的法医反积云,认知偏见和巨大的心理压力的负面影响。本文评估了现有的面部图像数据集,并提出了一种针对类似数字法医研究贡献的需求而定制的新数据集。这个小型,不同的DataSet为0到20岁的个人包含245个图像,并与FG-Net DataSet的82个唯一图像合并,从而实现了具有高图像分集和低年龄范围密度的327个图像。在IMDB-Wiki DataSet上预先培训的深度期望(DEX)算法测试新数据集。 16至20岁的年轻青少年和年龄较大的青少年/成年人的整体成果非常令人鼓舞 - 达到1.79年的MAE,但也表明0至10岁儿童的准确性需要进一步的工作。为了确定原型的功效,已经考虑了四个数字法医专家的有价值输入,以提高年龄估计结果。需要进一步的研究来扩展关于图像密度的数据集和性别和种族分集等因素的平等分布。
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面部分析模型越来越多地应用于对人们生活产生重大影响的现实应用中。但是,正如文献所表明的那样,自动对面部属性进行分类的模型可能会对受保护的群体表现出算法歧视行为,从而对个人和社会产生负面影响。因此,开发可以减轻面部分类器中意外偏见的技术至关重要。因此,在这项工作中,我们引入了一种新颖的学习方法,该方法将基于人类的主观标签和基于面部特征的数学定义的客观注释结合在一起。具体而言,我们从两个大规模的人类注销数据集中生成了新的客观注释,每个数据集都捕获了分析的面部特征的不同观点。然后,我们提出了一种合奏学习方法,该方法结合了接受不同类型注释的单个模型。我们对注释过程以及数据集分布提供了深入的分析。此外,我们从经验上证明,通过结合标签多样性,我们的方法成功地减轻了意外偏见,同时保持了下游任务的明显准确性。
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由于隐私,透明度,问责制和缺少程序保障的担忧,印度的面部加工系统的增加越来越多。与此同时,我们也很少了解这些技术如何在印度13.4亿种群的不同特征,特征和肤色上表现出来。在本文中,我们在印度脸部的数据集中测试四个商用面部加工工具的面部检测和面部分析功能。该工具在面部检测和性别和年龄分类功能中显示不同的错误率。与男性相比,印度女性面的性别分类错误率始终如一,最高的女性错误率为14.68%。在某些情况下,这种错误率远高于其他国籍的女性之前的研究表明。年龄分类错误也很高。尽管从一个人的实际年龄从一个人的实际年龄到10年来考虑到可接受的误差率,但年龄预测失败的速度为14.3%至42.2%。这些发现指向面部加工工具的准确性有限,特别是某些人口组,在采用此类系统之前需要更关键的思维。
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已显示现有的面部分析系统对某些人口统计亚组产生偏见的结果。由于其对社会的影响,因此必须确保这些系统不会根据个人的性别,身份或肤色歧视。这导致了在AI系统中识别和减轻偏差的研究。在本文中,我们封装了面部分析的偏置检测/估计和缓解算法。我们的主要贡献包括对拟议理解偏见的算法的系统审查,以及分类和广泛概述现有的偏置缓解算法。我们还讨论了偏见面部分析领域的开放挑战。
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在本文中,我们研究了视觉和热图像的性能和公平性,并将评估扩展到掩盖的合成图像。使用SeadyFace和Thermal掩码数据集,我们提出了一个过程来评估真实图像的公平性,并显示如何将同一过程应用于合成图像。随机猜测的人口统计差异为1.59,当识别性能提高到99.99 \%时,人口统计学差异为1.59。我们表明,固有的偏见数据集可以深深影响任何生物识别系统的公平性。偏见数据集的主要原因是由于数据收集过程而导致的类不平衡。为了解决不平衡的数据集,可以使用合成图像来增强样品的较少类,以生成更平衡的数据集,从而在训练机器学习系统时产生较小的偏见。对于支持生物特征的系统,公平性至关重要,而相关的公平,多样性和包容性(EDI)的相关概念非常适合生物识别技术公平性的概括,我们专注于3个最常见的人口统计组年龄,性别和种族。
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2019年,英国的移民和庇护室的上部法庭驳回了基于其他差异的生物识别系统产出的决定。在生物识别数据库中发现了庇护所寻求者的指纹,这与上诉人的账户相矛盾。法庭发现这一证据明确透明,否认庇护索赔。如今,生物识别系统的扩散正在围绕其政治,社会和道德意义塑造公众辩论。然而,虽然对移动控制的种族式使用这种技术的担忧一直在上升,但对生物识别行业的投资和创新正在增加大幅增加。此外,生物识别技术最近也已经采用了公平,以减轻生物识别学的偏见和歧视。然而,算法公平不能在破损或预期目的的情况下分配正义,这是为了区分,例如在边境部署的生物识别。在本文中,我们提供了最近关于生物识别公平性辩论的批判性阅读,并展示了其在机器学习和关键边界研究的公平研究中的局限性。在以前的公平演示中,我们证明了生物识别公平标准是数学上的互斥。然后,纸张继续验证说明公平的生物识别系统,通过从先前的作品中再现实验。最后,我们通过在边境的辩论中讨论生物识别性的公平性的政治。我们声称偏见和错误率对公民和寻求庇护者产生了不同的影响。公平已经在生物识别学室内黯然失色,专注于算法的人口偏见和伦理话语,而不是检查这些系统如何重现历史和政治不公正。
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自动面检测等计算机视觉应用用于各种目的,从解锁智能设备到跟踪监视的潜在感兴趣的人。这些申请的审计透露,他们倾向于对少数民族群体偏见,导致不公平和关于社会和政治结果。尽管随着时间的推移,但这些偏差尚未完全减轻,但实际上已经增加了年龄预测等任务。虽然这些系统审核了基准数据集,但有必要评估其对抗性投入的鲁棒性。在这项工作中,我们在多个系统和数据集上进行广泛的对手审核,并进行了许多关于观察 - 从以前的审计以来的一些任务对一些任务进行了准确性。虽然仍然对多个数据集的少数群体的个体仍然存在偏差,但更令人担忧的观察是这些偏差倾向于对少数群体的对抗意义进行过度发音。我们讨论了鉴于这些观察结果更广泛的社会影响以及关于如何共同应对这个问题的建议。
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The presence of bias in deep models leads to unfair outcomes for certain demographic subgroups. Research in bias focuses primarily on facial recognition and attribute prediction with scarce emphasis on face detection. Existing studies consider face detection as binary classification into 'face' and 'non-face' classes. In this work, we investigate possible bias in the domain of face detection through facial region localization which is currently unexplored. Since facial region localization is an essential task for all face recognition pipelines, it is imperative to analyze the presence of such bias in popular deep models. Most existing face detection datasets lack suitable annotation for such analysis. Therefore, we web-curate the Fair Face Localization with Attributes (F2LA) dataset and manually annotate more than 10 attributes per face, including facial localization information. Utilizing the extensive annotations from F2LA, an experimental setup is designed to study the performance of four pre-trained face detectors. We observe (i) a high disparity in detection accuracies across gender and skin-tone, and (ii) interplay of confounding factors beyond demography. The F2LA data and associated annotations can be accessed at http://iab-rubric.org/index.php/F2LA.
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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.
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Pseudo Artificial Intelligence bias (PAIB) is broadly disseminated in the literature, which can result in unnecessary AI fear in society, exacerbate the enduring inequities and disparities in access to and sharing the benefits of AI applications, and waste social capital invested in AI research. This study systematically reviews publications in the literature to present three types of PAIBs identified due to: a) misunderstandings, b) pseudo mechanical bias, and c) over-expectations. We discussed the consequences of and solutions to PAIBs, including certifying users for AI applications to mitigate AI fears, providing customized user guidance for AI applications, and developing systematic approaches to monitor bias. We concluded that PAIB due to misunderstandings, pseudo mechanical bias, and over-expectations of algorithmic predictions is socially harmful.
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使用卷积神经网络,面部属性(例如,年龄和吸引力)估算性能得到了大大提高。然而,现有方法在培训目标和评估度量之间存在不一致,因此它们可能是次优。此外,这些方法始终采用具有大量参数的图像分类或面部识别模型,其携带昂贵的计算成本和存储开销。在本文中,我们首先分析了两种最新方法(排名CNN和DLDL)之间的基本关系,并表明排名方法实际上是隐含的学习标签分布。因此,该结果首先将两个现有的最新方法统一到DLDL框架中。其次,为了减轻不一致和降低资源消耗,我们设计了一种轻量级网络架构,并提出了一个统一的框架,可以共同学习面部属性分发和回归属性值。在面部年龄和吸引力估算任务中都证明了我们的方法的有效性。我们的方法使用单一模型实现新的最先进的结果,使用36美元\倍,参数减少3美元,在面部年龄/吸引力估算上的推动速度为3美元。此外,即使参数的数量进一步降低到0.9m(3.8MB磁盘存储),我们的方法也可以实现与最先进的结果。
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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.
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我们介绍了Daisee,这是第一个多标签视频分类数据集,该数据集由112个用户捕获的9068个视频片段,用于识别野外无聊,混乱,参与度和挫败感的用户情感状态。该数据集具有四个级别的标签 - 每个情感状态都非常低,低,高和很高,它们是人群注释并与使用专家心理学家团队创建的黄金标准注释相关的。我们还使用当今可用的最先进的视频分类方法在此数据集上建立了基准结果。我们认为,黛西(Daisee)将为研究社区提供特征提取,基于上下文的推理以及为相关任务开发合适的机器学习方法的挑战,从而为进一步的研究提供了跳板。该数据集可在https://people.iith.ac.in/vineethnb/resources/daisee/daisee/index.html下载。
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Facial analysis systems have been deployed by large companies and critiqued by scholars and activists for the past decade. Many existing algorithmic audits examine the performance of these systems on later stage elements of facial analysis systems like facial recognition and age, emotion, or perceived gender prediction; however, a core component to these systems has been vastly understudied from a fairness perspective: face detection, sometimes called face localization. Since face detection is a pre-requisite step in facial analysis systems, the bias we observe in face detection will flow downstream to the other components like facial recognition and emotion prediction. Additionally, no prior work has focused on the robustness of these systems under various perturbations and corruptions, which leaves open the question of how various people are impacted by these phenomena. We present the first of its kind detailed benchmark of face detection systems, specifically examining the robustness to noise of commercial and academic models. We use both standard and recently released academic facial datasets to quantitatively analyze trends in face detection robustness. Across all the datasets and systems, we generally find that photos of individuals who are $\textit{masculine presenting}$, $\textit{older}$, of $\textit{darker skin type}$, or have $\textit{dim lighting}$ are more susceptible to errors than their counterparts in other identities.
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As facial recognition systems are deployed more widely, scholars and activists have studied their biases and harms. Audits are commonly used to accomplish this and compare the algorithmic facial recognition systems' performance against datasets with various metadata labels about the subjects of the images. Seminal works have found discrepancies in performance by gender expression, age, perceived race, skin type, etc. These studies and audits often examine algorithms which fall into two categories: academic models or commercial models. We present a detailed comparison between academic and commercial face detection systems, specifically examining robustness to noise. We find that state-of-the-art academic face detection models exhibit demographic disparities in their noise robustness, specifically by having statistically significant decreased performance on older individuals and those who present their gender in a masculine manner. When we compare the size of these disparities to that of commercial models, we conclude that commercial models - in contrast to their relatively larger development budget and industry-level fairness commitments - are always as biased or more biased than an academic model.
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