Papers
8
Total Citations
36
H-Index
3
About
Kyu-Dae Ban is a researcher whose work lies at the intersection of human-robot interaction, audio-visual perception, and intelligent service robotics. His primary contributions focus on enabling robots to perceive and interact with humans naturally through multimodal sensory integration. Ban’s most influential work, "Sound Source Localization Based on Audio-visual Information for Intelligent Service Robots" (2007, 12 citations), pioneered the fusion of auditory and visual data to improve sound localization—a critical capability for robots operating in dynamic environments. He further advanced this field with studies on Kalman filter-based impulse sound tracking and GCC-PHAT-based localization methods, which enhanced the accuracy and robustness of robot auditory systems. In parallel, Ban made notable contributions to face recognition under challenging robotic conditions, systematically evaluating eigenface, fisherface, and ICA-based approaches against variations in illumination and distance. His work on normalized cross-correlation for face image registration and fusion techniques combining camera and microphone data for user identification demonstrates a sustained commitment to building reliable, context-aware service robots. As part of Korea’s Ubiquitous Robot Companion initiative, Ban’s research has helped lay the groundwork for robots that can see, hear, and respond to humans in real-world settings, bridging the gap between perception and natural interaction.
Research Focus
Key Achievements
Top Papers
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- 2Face image registration methods using Normalized Cross Correlation7 citations · 2008
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