Attention-Aware Recognition of Activities of Daily Living Based on Eye Gaze Tracking

Madhusanka, B.G.D.A.; Ramadass, Sureswaran; Rajagopal, Premkumar; Herath, H.M.R.P.

doi:10.1007/978-981-16-8488-3_8

Cited by 5 publications

(2 citation statements)

References 70 publications

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“…Thai-Viet Dang, A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet vertical axes. This image preprocessing will help the accuracy of face detection and recognition later [52][53][54][55][56][57][58].…”

Section: Head Pose Estimationmentioning

confidence: 99%

A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet

Dang,

Tran

2023

Journal of Robotics and Control

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Artificial Intelligence and IoT have always attracted a lot of attention from scholars and researchers because of their high applicability, which make them a typical technology of the Fourth Industrial Revolution. The hallmark of AI is its self-learning ability, which enables computers to predict and analyze complex data such as bio data (fingerprints, irises, and faces), voice recognition, text processing. Among those application, the face recognition is under intense research due to the demand in users’ identification. This paper proposes a new, secured, two-step solution for an identification system that uses MTCNN and FaceNet networks enhanced with head pose estimation of the users. The model's accuracy ranges from 92% to 95%, which make it competitive with recent research to demonstrate the system's usability.

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Section: Head Pose Estimationmentioning

confidence: 99%

A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet

Dang,

Tran

2023

Journal of Robotics and Control

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“…Human-Computer Interaction (HCI) gets a nonverbal communication boost in [27], thanks to an eye-view-based nonverbal communication paradigm. Traditional gaze detection systems offer remarkable performance and endurance.…”

Section: Literature Reviewmentioning

confidence: 99%

Pupil Position by an Improved Technique of YOLO Network for Eye Tracking Application

S. Mary Rexcy Asha

2023

IJRITCC

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This Eye gaze following is the real-time collection of information about a person's eye movements and the direction of their look. Eye gaze trackers are devices that measure the locations of the pupils to detect and track changes in the direction of the user's gaze. There are numerous applications for analyzing eye movements, from psychological studies to human-computer interaction-based systems and interactive robotics controls. Real-time eye gaze monitoring requires an accurate and reliable iris center localization technique. Deep learning technology is used to construct a pupil tracking approach for wearable eye trackers in this study. This pupil tracking method uses deep-learning You Only Look Once (YOLO) model to accurately estimate and anticipate the pupil's central location under conditions of bright, natural light (visible to the naked eye). Testing pupil tracking performance with the upgraded YOLOv7 results in an accuracy rate of 98.50% and a precision rate close to 96.34% using PyTorch.

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Monitoring the Impact of Stress on Facial Skin Using Affective Computing

Herath

Karunasena

Mittal

2022

Lecture Notes on Data Engineering and Communications Technologies

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Attention-Aware Recognition of Activities of Daily Living Based on Eye Gaze Tracking

Cited by 5 publications

References 70 publications

A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet

A Secured, Multilevel Face Recognition based on Head Pose Estimation, MTCNN and FaceNet

Pupil Position by an Improved Technique of YOLO Network for Eye Tracking Application

Monitoring the Impact of Stress on Facial Skin Using Affective Computing

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