Jogendra Garain

National Institute of Technology Durgapur

Papers

Total Citations

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About

Jogendra Garain is a researcher whose work lies at the intersection of computer vision and cognitive science, with a particular focus on visual attention modeling. His most cited paper, "Guiding attention of faces through graph based visual saliency (GBVS)" (2019), which has garnered 8 citations, introduces a novel approach to understanding how human gaze is directed toward faces in images. By adapting the classic Graph-Based Visual Saliency (GBVS) algorithm, Garain demonstrates how computational models can be refined to prioritize facial regions, a critical step for applications in human-computer interaction, social robotics, and assistive technologies. This contribution not only advances the theoretical understanding of bottom-up and top-down attention mechanisms but also provides a practical tool for improving the performance of face detection and recognition systems. Garain’s work bridges the gap between biological vision and machine perception, offering insights that are valuable for both researchers developing more human-like AI and engineers building intuitive interfaces. His research underscores the importance of integrating domain-specific knowledge—such as the saliency of faces—into generic visual attention frameworks, marking a meaningful step toward more context-aware and socially intelligent computer vision systems.