Sumit Jha

About

Sumit Jha is a researcher whose work lies at the intersection of computer vision and human-computer interaction (HCI), with a particular focus on gaze estimation. His most-cited paper, "Estimation of Gaze Region Using Two Dimensional Probabilistic Maps Constructed Using Convolutional Neural Networks" (2019, 8 citations), introduces a novel appearance-based approach that leverages convolutional neural networks to predict where a user is looking. By constructing two-dimensional probabilistic maps, Jha’s method enhances the accuracy and robustness of gaze region estimation—a critical capability for applications ranging from driver distraction detection and social interaction analysis to human-robot interaction and educational technology. This contribution addresses a key challenge in HCI: enabling machines to understand human attention non-invasively. While his citation count reflects a growing body of work in this specialized area, Jha’s research is notable for its practical focus on real-world usability, bridging the gap between deep learning models and intuitive user interfaces. His work continues to inform advancements in assistive technologies and adaptive systems, making him a promising voice in the field of intelligent human-centered computing.

Research Focus

Key Achievements

Top Papers

1. 1
   Estimation of Gaze Region Using Two Dimensional Probabilistic Maps Constructed Using Convolutional Neural Networks

   8 citations · 2019

Key Collaborators

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