Sadia Afroze

Green University of Bangladesh, Chittagong University of Engineering & Technology

Papers

Total Citations

H-Index

About

Sadia Afroze is a rising researcher in computer vision, with a focus on human behavior understanding and human-computer interaction. Her work centers on developing deep learning frameworks to interpret visual cues, particularly eye gaze and head pose, enabling machines to understand human attention in complex, multi-object environments. Her most cited paper, "DeepFocus: A visual focus of attention detection framework using deep learning in multi-object scenarios" (2022, 7 citations), introduces a novel approach for recognizing visual focus of attention (VFoA), a critical capability for applications in human-robot interaction and assistive technologies. This work demonstrates her ability to tackle the challenging problem of inferring where a person is looking in cluttered scenes. Additionally, her paper "Head Pose Classification Based on Deep Convolution Networks" (2021, 3 citations) contributes to the foundational task of estimating head orientation, a key component for gaze analysis. Though early in her career, Afroze’s research is already establishing a foundation for more intuitive and responsive AI systems, promising significant impact in fields ranging from collaborative robotics to accessibility technology.