This study offers an in-depth analysis of the application and implications of
the National Institute of Standards and Technology's AI Risk Management
Framework (NIST AI RMF) within the domain of surveillance technologies,
particularly facial recognition technology. Given the inherently high-risk and
consequential nature of facial recognition systems, our research emphasizes the
critical need for a structured approach to risk management in this sector. The
paper presents a detailed case study demonstrating the utility of the NIST AI
RMF in identifying and mitigating risks that might otherwise remain unnoticed
in these technologies. Our primary objective is to develop a comprehensive risk
management strategy that advances the practice of responsible AI utilization in
feasible, scalable ways. We propose a six-step process tailored to the specific
challenges of surveillance technology that aims to produce a more systematic
and effective risk management practice. This process emphasizes continual
assessment and improvement to facilitate companies in managing AI-related risks
more robustly and ensuring ethical and responsible deployment of AI systems.
Additionally, our analysis uncovers and discusses critical gaps in the current
framework of the NIST AI RMF, particularly concerning its application to
surveillance technologies. These insights contribute to the evolving discourse
on AI governance and risk management, highlighting areas for future refinement
and development in frameworks like the NIST AI RMF.

在监控技术领域，特别是人脸识别技术中，本研究对国家标准与技术研究院的人工智能风险管理框架（NIST AI RMF）的应用和影响进行了深入分析。通过案例研究，论文展示了 NIST AI RMF 在识别和减轻这些技术中可能被忽视的风险方面的实用性。我们的主要目标是制定一套面向监控技术的综合风险管理策略，以可行、可扩展的方式推进负责任的人工智能利用实践。此过程强调持续评估和改进，以帮助公司更加有力地管理与人工智能相关的风险，并确保道德和负责任地部署人工智能系统。此外，我们的分析揭示并讨论了国家标准与技术研究院的人工智能风险管理框架目前存在的关键缺陷，特别是在监控技术应用方面。这些洞察为人工智能治理和风险管理的讨论做出了贡献，突出了 NIST AI RMF 等框架未来改进和发展的方向。

NIST AI 风险管理框架在监控技术中的应用

Application of the NIST AI Risk Management Framework to Surveillance  Technology

Automatic methods for detecting presentation attacks are essential to ensure
the reliable use of facial recognition technology. Most of the methods
available in the literature for presentation attack detection (PAD) fails in
generalizing to unseen attacks. In recent years, multi-channel methods have
been proposed to improve the robustness of PAD systems. Often, only a limited
amount of data is available for additional channels, which limits the
effectiveness of these methods. In this work, we present a new framework for
PAD that uses RGB and depth channels together with a novel loss function. The
new architecture uses complementary information from the two modalities while
reducing the impact of overfitting. Essentially, a cross-modal focal loss
function is proposed to modulate the loss contribution of each channel as a
function of the confidence of individual channels. Extensive evaluations in two
publicly available datasets demonstrate the effectiveness of the proposed
approach.

本研究提出了一种采用 RGB 和深度通道的新框架，结合一种新的损失函数用于检测人脸识别中的攻击，通过多通道提取信息，减小过拟合的影响并提高鲁棒性。新的跨模态焦点损失函数通过基于信道的置信度来调制每个信道的损失贡献，通过两个公开的数据集进行广泛评估，证明了该方法的有效性。

RGBD 人脸反欺诈的跨模态焦点损失

Cross Modal Focal Loss for RGBD Face Anti-Spoofing

A key step to driver safety is to observe the driver's activities with the
face being a key step in this process to extracting information such as head
pose, blink rate, yawns, talking to passenger which can then help derive higher
level information such as distraction, drowsiness, intent, and where they are
looking. In the context of driving safety, it is important for the system
perform robust estimation under harsh lighting and occlusion but also be able
to detect when the occlusion occurs so that information predicted from occluded
parts of the face can be taken into account properly. This paper introduces the
Occluded Stacked Hourglass, based on the work of original Stacked Hourglass
network for body pose joint estimation, which is retrained to process a
detected face window and output 68 occlusion heat maps, each corresponding to a
facial landmark. Landmark location, occlusion levels and a refined face
detection score, to reject false positives, are extracted from these heat maps.
Using the facial landmark locations, features such as head pose and eye/mouth
openness can be extracted to derive driver attention and activity. The system
is evaluated for face detection, head pose, and occlusion estimation on various
datasets in the wild, both quantitatively and qualitatively, and shows
state-of-the-art results.

本研究介绍了基于 Stacked Hourglass 网络的 Occluded Stacked Hourglass 模型，利用检测到的面部窗口，输出 68 个遮挡热力图，从而可以提取脸部特征，如头部姿势以及眼睛 / 嘴巴的张合程度，以便推导驾驶员的注意力和行为模式，模型的遮挡估计和头部姿势能力达到了最新的技术水平。