Mohammed Moshiul Hoque
Saitama University, Chittagong University of Engineering & Technology, Human Media
Papers
19
Total Citations
136
H-Index
7
About
Mohammed Moshiul Hoque is a researcher whose work sits at the intersection of human-robot interaction (HRI), social robotics, and computer vision, with a particular focus on attention control and gaze-based communication. His research has systematically explored how robots can attract, recognize, and direct human attention through nonverbal behaviors — most notably gaze cues — across a range of social and multi-party situations. Among his most influential contributions is the development of empirical and vision-based frameworks that enable robots to establish eye contact and shift a person's attention even in challenging scenarios, such as when individuals are not initially facing the robot or are deeply engaged in another task. His 2011 foundational framework for robotic attention control, alongside subsequent work on duplex eye contact mechanisms and deep learning-based visual focus of attention detection, demonstrates a sustained research trajectory that has grown increasingly sophisticated over a decade. His 2022 DeepFocus system reflects a meaningful pivot toward leveraging deep learning for real-world HCI applications. With cumulative citations across ten key publications, Hoque has made a quiet but meaningful contribution to making robots more socially aware and communicatively capable — a cornerstone challenge in building robots that can operate naturally alongside humans.
Research Focus
Key Achievements
Top Papers
- 1An Empirical Framework to Control Human Attention by Robot16 citations · 2011
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- 4Controlling human attention through robot's gaze behaviors12 citations · 2011
- 5Vision-based attention control system for socially interactive robots10 citations · 2012
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- 10Model for controlling a target human's attention in multi-party settings6 citations · 2012