DFIC：面向自动ICAO合规验证的平衡面部图像数据集
DFIC: Towards a balanced facial image dataset for automatic ICAO compliance verification
AI总结：
DFIC数据集通过平衡的面部图像分布和空间注意力机制提升ICAO合规验证的自动化水平。
摘要：
确保机器可读旅行证件（MRTDs）中面部图像符合ISO/IEC和ICAO标准对于可靠的身份验证至关重要，但目前的手动检查方法在高需求环境下效率低下。本文介绍了DFIC数据集，这是一个包含约58,000张标注图像和2706个视频的新型全面面部图像数据集，涵盖广泛不合规条件以及合规肖像。我们的数据集比现有公开数据集具有更平衡的人口统计分布，其中一分区几乎均匀分布，有助于开发自动ICAO合规验证方法。使用DFIC，我们微调了一种新的方法，该方法严重依赖于空间注意力机制以自动验证ICAO合规性要求，并将其与面向ICAO合规验证的最先进方法进行比较，证明了改进的结果。DFIC数据集现已公开（https://github.com/visteam-isr-uc/DFIC）用于新模型的训练和验证，提供前所未有的面部多样性，将提高系统对内在多样化的面部和道具组合的鲁棒性和适应性。这些结果强调了DFIC在增强自动ICAO合规方法方面的潜力，但它也可以用于许多其他旨在提高面部识别系统安全、隐私和公平性的应用。
英文摘要：
Ensuring compliance with ISO/IEC and ICAO standards for facial images in machine-readable travel documents (MRTDs) is essential for reliable identity verification, but current manual inspection methods are inefficient in high-demand environments. This paper introduces the DFIC dataset, a novel comprehensive facial image dataset comprising around 58,000 annotated images and 2706 videos of more than 1000 subjects, that cover a broad range of non-compliant conditions, in addition to compliant portraits. Our dataset provides a more balanced demographic distribution than the existing public datasets, with one partition that is nearly uniformly distributed, facilitating the development of automated ICAO compliance verification methods. Using DFIC, we fine-tuned a novel method that heavily relies on spatial attention mechanisms for the automatic validation of ICAO compliance requirements, and we have compared it with the state-of-the-art aimed at ICAO compliance verification, demonstrating improved results. DFIC dataset is now made public (https://github.com/visteam-isr-uc/DFIC) for the training and validation of new models, offering an unprecedented diversity of faces, that will improve both robustness and adaptability to the intrinsically diverse combinations of faces and props that can be presented to the validation system. These results emphasize the potential of DFIC to enhance automated ICAO compliance methods but it can also be used in many other applications that aim to improve the security, privacy, and fairness of facial recognition systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
