Farrukh Nagi

About

Farrukh Nagi is a pioneer in the intersection of computer vision and multi-robot systems, best known for his foundational work in deep learning-based hand gesture recognition. His landmark 2011 paper, "Max-pooling convolutional neural networks for vision-based hand gesture recognition," has accumulated over 648 citations, establishing a cornerstone for real-time human-robot interaction (HRI) and sign language recognition. Nagi’s major contributions include the development of Convolutional Neural Support Vector Machines (CNSVMs), a hybrid classifier that fuses CNNs with SVMs for robust visual pattern recognition in multi-robot environments. He also introduced Convolutional Max-Pooling (CMP), a novel online feature extraction method enabling incremental learning of gestures for human-swarm interaction. Beyond vision-based interfaces, Nagi has advanced adaptive control systems, designing an on-line adaptive fuzzy switching controller for industrial SCARA robots. His work bridges the gap between deep learning and robotics, enabling intuitive, real-time control of both individual and swarm robotic systems. Through his innovative hybrid architectures and real-time learning frameworks, Nagi has significantly shaped the field of intelligent robotic interaction.

Research Focus

Key Achievements

Top Papers

1. 1
   Max-pooling convolutional neural networks for vision-based hand gesture recognition

   648 citations · 2011
2. 2
   Convolutional Neural Support Vector Machines: Hybrid Visual Pattern Classifiers for Multi-robot Systems

   49 citations · 2012
3. 3
   Online feature extraction for the incremental learning of gestures in human-swarm interaction

   6 citations · 2014
4. 4
   On-line adaptive fuzzy switching controller for SCARA robot

   3 citations · 2011

Key Collaborators

Contact & Links