Lightweight Face Anti-Spoofing Network for Telehealth Applications

Lin, Jiun-Da; Lin, Hung-Hsiang; Dy, Jilyan; Chen, Jun-Cheng; Tanveer, M.; Razzak, Muhammad Imran; Hua, Kai-Lung

doi:10.1109/jbhi.2021.3107735

Cited by 10 publications

(6 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…With Arcface algorithm the Innovative facial classification of images is more accurate as they differentiate the images with image boundaries (Lin et al 2021). Arcface is an optimized algorithm for recognizing images as this algorithm introduces the Arc Loss function which optimizes the angular distance in an image (Baltanas, Ruiz-Sarmiento, and Gonzalez-Jimenez 2021).…”

Section: Discussionmentioning

confidence: 99%

Realtime Criminal Face Recognition System based on Innovative Facial Classification to Improve Accuracy using Arcface over Deepface

2022

pnr

View full text Add to dashboard Cite

The objective of this study is to use face recognition technology to identify the suspects through facial biometrics which helps in identification of criminals in a better way. LBPH algorithm is used for the face recognition method. Materials and Methods: Face detection for identifying the criminals is performed using Arcface (N=10) over Deepface model (N=10) with Gpower of 80% and alpha =0.05, split size of 70% and 30 % for training and testing model respectively. Results: It is found that the accuracy of Arcface is 92.50% which is higher than the Deepface model 91.90% and attained the significance value of p=0.0118 (p<0.05), showing that there is a significant difference between the groups.. Conclusion: For the face identification purpose Arcface algorithm is preferred than Deepface algorithm.

show abstract

Section: Discussionmentioning

confidence: 99%

Realtime Criminal Face Recognition System based on Innovative Facial Classification to Improve Accuracy using Arcface over Deepface

2022

pnr

View full text Add to dashboard Cite

show abstract

“…Researchers and developers turned their attention to redrawing the boundaries between AI or nonhuman control versus human control in the data deluge following the COVID-19 pandemic, which includes enormous amounts of both personal and medical stored during telehealth sessions (Lin et al, 2022). One popular application of camera technologies which intersects with telehealth is facial recognition software, which treats a human face like geographic topography, capitalizing on the unique undulations in features to determine if the human is the authentic user.…”

Section: Ai In Virtual Settingsmentioning

confidence: 99%

“…Another application of AI is the detection of depression via telehealth, the treatment of depression using a machine learning algorithm to detect nonverbal cues in facial expressions (Stratou & Morency, 2017). However, a wrinkle exists; the use of AI to develop these software programs and functionalities created a lighted pathway for hackers and phishing scammers to augment real data into copies of faces that can be used to compromise private information (Lin et al, 2022). This scenario is foreboding for the future possibilities of machine-based attacks on humans, leveraging humanistic qualities that computers and models do not possess, such as physical features.…”

Section: Ai In Virtual Settingsmentioning

confidence: 99%

When to err is inhuman: An examination of the influence of artificial intelligence‐driven nursing care on patient safety

2023

View full text Add to dashboard Cite

Artificial intelligence, as a nonhuman entity, is increasingly used to inform, direct, or supplant nursing care and clinical decision‐making. The boundaries between human‐ and nonhuman‐driven nursing care are blurred with the advent of sensors, wearables, camera devices, and humanoid robots at such an accelerated pace that the critical evaluation of its influence on patient safety has not been fully assessed. Since the pivotal release of To Err is Human, patient safety is being challenged by the dynamic healthcare environment like never before, with nursing at a critical juncture to steer the course of artificial intelligence integration in clinical decision‐making. This paper presents an overview of artificial intelligence and its application in healthcare and highlights the implications which affect nursing as a profession, including perspectives on nursing education and training recommendations. The legal and policy challenges which emerge when artificial intelligence influences the risk of clinical errors and safety issues are discussed.

show abstract

“…Paper [ 30 ] proposes a Lightweight Face Anti-Spoofing Network for Telehealth Applications that allows doctors and patients to schedule consultations, share medical information through secure user authentication of face recognition. Paper [ 31 ] proposes OpenHealthQ, which is an OpenFlow based traffic-shaping model using OpenFlow Queues to handle the data from healthcare.…”

Section: Related Workmentioning

confidence: 99%

A machine learning and blockchain based secure and cost-effective framework for minor medical consultations

Hassija

Ratnakumar

Chamola

et al. 2022

Sustainable Computing: Informatics and Systems

View full text Add to dashboard Cite

Lightweight Face Anti-Spoofing Network for Telehealth Applications

Cited by 10 publications

References 55 publications

Realtime Criminal Face Recognition System based on Innovative Facial Classification to Improve Accuracy using Arcface over Deepface

Realtime Criminal Face Recognition System based on Innovative Facial Classification to Improve Accuracy using Arcface over Deepface

When to err is inhuman: An examination of the influence of artificial intelligence‐driven nursing care on patient safety

A machine learning and blockchain based secure and cost-effective framework for minor medical consultations

Contact Info

Product

Resources

About