Jyoti Jasekar
Papers
1
Total Citations
6
H-Index
1
About
Jyoti Jasekar is a researcher at the intersection of computer vision, affective computing, and human-robot interaction. Her work focuses on enabling machines to perceive and respond to human emotional states, with a particular emphasis on facial expression recognition. Her most-cited paper, "Extended LBP based Facial Expression Recognition System for Adaptive AI Agent Behaviour" (2018, 6 citations), introduces a novel system that uses extended Local Binary Patterns to automatically recognize facial emotions and adapt the behavior of an AI agent accordingly. This contribution is significant for applications in healthcare, surveillance, and interactive robotics, where context-aware and empathetic machine responses are critical. Though early in her career, Jasekar's work demonstrates a clear commitment to bridging the gap between raw visual data and intelligent, adaptive systems. Her research holds promise for creating more natural and responsive human-machine interfaces, laying groundwork for future advancements in emotionally intelligent AI.
Research Focus
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Top Papers
- 1