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10.1109/JBHI.2021.3107735 http://scihub22266oqcxt.onion/10.1109/JBHI.2021.3107735 34432642!ä!34432642 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\34432642.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       IEEE+J+Biomed+Health+Inform 2022 ; 26 (5): 1987-1996 Nephropedia Template TP {{tp|p=34432642|t=2022. Lightweight Face Anti-Spoofing Network for Telehealth Applications |pdf=|usr=}}{{34432642}} gab.com Text Lightweight Face Anti-Spoofing Network for Telehealth Applications JO: IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=34432642 #FOAvip 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. Twit Text FOAVip Lightweight Face Anti-Spoofing Network for Telehealth Applications ????IEEE J Biomed Health Inform http://www.kidney.de/pget.php?&tw=1&pmid=34432642 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34432642 #FOAvip English Wikipedia <ref>{{Cite pmid|34432642}}</ref> Lightweight Face Anti-Spoofing Network for Telehealth Applications #MMPMID34432642 Lin JD; Lin HH; Dy J; Chen JC; Tanveer M; Razzak I; Hua KL IEEE J Biomed Health Inform 2022[May]; 26 (5): 1987-1996 PMID34432642show ga 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. |*COVID-19[MESH] |*Telemedicine[MESH] |Computer Security[MESH] |Face[MESH]Lightweight Face Anti-Spoofing Network for Telehealth Applications (epmc).pdf DeepDyve Pubget Overpricing