Soumyajit Saha
Papers
1
Total Citations
2
H-Index
1
About
Soumyajit Saha is a researcher at the forefront of computer vision and affective computing, with a particular focus on facial expression recognition from thermal imaging. His work addresses the critical challenge of automated emotion detection in human-computer interaction systems, where traditional visible-light approaches often fall short. Saha’s most cited paper, “Deep Feature Selection Using Moth-Flame Optimization for Facial Expression Recognition from Thermal Images” (2022, 2 citations), introduces a novel hybrid methodology that combines deep learning with nature-inspired optimization algorithms. By applying moth-flame optimization to select the most discriminative deep features, his research significantly improves recognition accuracy in thermal facial images—a modality that offers robustness to illumination changes and can capture genuine physiological responses. This contribution is particularly valuable for applications in interactive gaming, data-driven animation, sociable robotics, and other interactive systems where reliable emotion sensing is essential. Though early in his career, Saha’s work demonstrates a promising integration of evolutionary computation and deep learning, establishing a foundation for more resilient and privacy-preserving affective computing technologies.
Research Focus
Key Achievements
Top Papers
- 1