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

Jawad Nagi, Frederick Ducatelle, Gianni A. Di Caro, Dan Cireşan, Ueli Meier, Alessandro Giusti, Farrukh Nagi, Jurgen Schmidhuber, Luca Maria Gambardella

Research output: Chapter in Book/Report/Conference proceedingConference contribution

509 Scopus citations

Abstract

Automatic recognition of gestures using computer vision is important for many real-world applications such as sign language recognition and human-robot interaction (HRI). Our goal is a real-time hand gesture-based HRI interface for mobile robots. We use a state-of-the-art big and deep neural network (NN) combining convolution and max-pooling (MPCNN) for supervised feature learning and classification of hand gestures given by humans to mobile robots using colored gloves. The hand contour is retrieved by color segmentation, then smoothened by morphological image processing which eliminates noisy edges. Our big and deep MPCNN classifies 6 gesture classes with 96% accuracy, nearly three times better than the nearest competitor. Experiments with mobile robots using an ARM 11 533MHz processor achieve real-time gesture recognition performance. © 2011 IEEE.
Original languageEnglish (US)
Title of host publication2011 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2011
Pages342-347
Number of pages6
DOIs
StatePublished - Dec 1 2011
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2022-09-14

Fingerprint

Dive into the research topics of 'Max-pooling convolutional neural networks for vision-based hand gesture recognition'. Together they form a unique fingerprint.

Cite this