Papers
1
Total Citations
25
H-Index
1
About
Abir Sen is a researcher at the forefront of human-computer interaction, specializing in computer vision and deep learning for gesture-based interfaces. His most impactful work introduces a novel hand gesture detection and recognition system that leverages an ensemble-based convolutional neural network (CNN), achieving robust performance in real-world scenarios. This paper, published in 2022 and garnering 25 citations, addresses critical challenges in dynamic gesture recognition by combining multiple CNN architectures to enhance accuracy and reduce false positives—a key step toward intuitive, touchless control systems for applications ranging from virtual reality to assistive technologies. Sen’s contributions lie in advancing ensemble learning techniques for spatiotemporal gesture data, demonstrating how model diversity can overcome limitations of single-network approaches. His work has been recognized for its practical implications, offering a scalable framework that balances computational efficiency with high recognition rates. By pushing the boundaries of how machines interpret human motion, Abir Sen is shaping the future of seamless, natural interaction between people and digital environments.
Research Focus
Key Achievements
Top Papers
- 1