Face Attention Tracker: A Self-Attention Mechanism for Monitoring Human Engagement

Author(s)	Jwalin Thaker
Country	India
Abstract	With a lot of meetings and classes for employees and students moving online through video conferencing tools during the pandemic, a lot of emphasis and research has been put into tools and technologies to capture engagement and attention of employees and students. This paper presents an enhanced face-tracking attention mechanism designed to specifically monitor and analyze this in various settings. By leveraging computer vision techniques and machine learning algorithms, this architecture can detect facial features, track eye movements, and interpret attention patterns in real-time. The proposed mechanism offers administrators valuable insights into engagement levels, enabling them to adapt marketing, governing or teaching strategies accordingly. Experimental results demonstrate the effectiveness of our approach, achieving high accuracy in attention detection across diverse environments. This technology has significant implications for improving attention and engagement outcomes through personalized learning experiences and targeted interventions.
Field	Engineering
Published In	Volume 3, Issue 4, July-August 2021
Published On	2021-07-08
DOI	https://doi.org/10.36948/ijfmr.2021.v03i04.42911
Short DOI	https://doi.org/g9gdn9

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International Journal For Multidisciplinary Research