具有照明意识的蒙面检测

Tran Hiep Dinh, Quang Manh Doan, N. Trung, Diep N. Nguyen, Chin-Teng Lin
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引用次数: 1

摘要

在过去两年中,许多国家实施了口罩强制规定,作为限制Covid-19传播的一种简单而有效的方法。除了当局关于在公共场合使用口罩的指导外,还制定了许多基于视觉的方法来帮助监测口罩佩戴情况。尽管已经获得了有希望的结果,但基于视觉的被遮挡人脸检测仍然存在一些挑战,主要是由于覆盖光照条件、物体尺度、掩模类型或遮挡水平的足够变化的高质量数据集不足。在本文中,我们研究了一个轻量级的蒙面检测系统在不同照明条件下的有效性,以及通过使用图像增强算法和照明感知分类器来增强其性能的可能性。首先介绍了在不同光照条件下戴口罩和不戴口罩的人体受试者数据集。然后在采集的数据集上训练照明感知分类器,在考虑图像增强算法的情况下,根据检测精度的差异自动对分类器进行标记。实验结果表明,结合光照感知和图像增强算法的蒙面人脸检测系统在准确率、F1-score和AP-M方面的性能分别提高了8.6%、7.4%和8.5%。
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Masked Face Detection with Illumination Awareness
Mask mandate has been applied in many countries in the last two years as a simple but effective way to limit the Covid-19 transmission. Besides the guidance from authorities regarding mask use in public, numerous vision-based approaches have been developed to aid with the monitoring of face mask wearing. Despite promising results have been obtained, several challenges in vision-based masked face detection still remain, primarily due to the insufficient of a quality dataset covering adequate variations in lighting conditions, object scales, mask types, or occlusion levels. In this paper, we investigate the effectiveness of a lightweight masked face detection system under different lighting conditions and the possibility of enhancing its performance with the employment of an image enhancement algorithm and an illumination awareness classifier. A dataset of human subjects with and without face masks in different lighting conditions is first introduced. An illumination awareness classifier is then trained on the collected dataset, the labeling of which is processed automatically based on the difference in detection accuracy when an image enhancement algorithm is taken into account. Experimental results have shown that the combination of the masked face detection system with the illumination awareness and an image enhancement algorithm can boost the system performance to up to 8.6%, 7.4%, and 8.5% in terms of Accuracy, F1-score, and AP-M, respectively.
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