Online healthcare applications have grown more popular over the years. For instance, telehealth is an online healthcare application that allows patients and doctors to schedule consultations, prescribe medication, share medical documents, and monitor health conditions conveniently. Apart from this, telehealth can also be used to store a patient's personal and medical information. With its rise in usage due to COVID-19, given the amount of sensitive data it stores, security measures are necessary. A simple way of making these applications more secure is through user authentication. One of the most common and often used authentications is face recognition. It is convenient and easy to use. However, face recognition systems are not foolproof. They are prone to malicious attacks like printed photos, paper cutouts, replayed videos, and 3D masks. The goal of face anti-spoofing is to differentiate real users (live) from attackers (spoof). Although effective in terms of performance, existing methods use a significant amount of parameters, making them resource-heavy and unsuitable for handheld devices. Apart from this, they fail to generalize well to new environments like changes in lighting or background. This paper proposes a lightweight face anti-spoofing framework that does not compromise on performance. Our proposed method achieves good performance with the help of an ArcFace Classifier (AC). The AC encourages differentiation between spoof and live samples by making clear boundaries between them. With clear boundaries, classification becomes more accurate. We further demonstrate our model's capabilities by comparing the number of parameters, FLOPS, and performance with other state-of-the-art methods.

中文翻译：

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用于远程医疗应用的轻量级人脸反欺骗网络


多年来，在线医疗保健应用程序变得越来越流行。例如，远程医疗是一种在线医疗保健应用程序，允许患者和医生方便地安排咨询、开药、共享医疗文件和监控健康状况。除此之外，远程医疗还可用于存储患者的个人信息和医疗信息。由于 COVID-19，其使用量有所增加，考虑到其存储的敏感数据量，安全措施是必要的。使这些应用程序更加安全的一个简单方法是通过用户身份验证。最常见和最常用的身份验证之一是人脸识别。它方便且易于使用。然而，人脸识别系统并非万无一失。它们很容易受到恶意攻击，例如打印的照片、剪纸、重播的视频和 3D 面具。人脸反欺骗的目标是区分真实用户（实时）和攻击者（欺骗）。尽管在性能方面有效，但现有方法使用大量参数，导致资源消耗大且不适合手持设备。除此之外，它们无法很好地泛化到新环境，例如照明或背景的变化。本文提出了一种不影响性能的轻量级人脸反欺骗框架。我们提出的方法在 ArcFace 分类器 (AC) 的帮助下实现了良好的性能。 AC 通过在欺骗样本和活样本之间明确界限来鼓励区分它们。边界清晰，分类更加准确。我们通过将参数数量、FLOPS 和性能与其他最先进的方法进行比较，进一步证明了我们模型的功能。