Example：10.1021/acsami.1c06204 or Chem. Rev., 2007, 107, 2411-2502
Lightweight Face Anti-Spoofing Network for Telehealth Applications. IEEE Journal of Biomedical and Health Informatics (IF5.772), Pub Date : 2021-08-25, DOI: 10.1109/jbhi.2021.3107735 Jiun-Da Lin,Hung-Hsiang Lin,Jilyan Dy,Jun-Cheng Chen,M Tanveer,Imran Razzak,Kai-Lung Hua
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 patients personal and medical information. Given the amount of sensitive data it stores,security measures are necessary. With its rise in usage due to COVID-19,its usefulness may be undermined if security issues are not addressed. 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,re-played videos,and 3D masks. In order to counter this,multiple face anti-spoofing methods have been proposed. 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. A lightweight model is critical for applications like telehealth that run on handheld devices. 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 models capabilities by comparing the number of parameters,FLOPS,and performance with other state-of-the-art methods.