Paper Info
Title
Supervised Training Based Hand Gesture Recognition System
Abstract
We have developed a hand gesture recognition system, based on the shape analysis of static gestures, for Human Computer Interaction purposes. Our appearance-based recognition uses modified Fourier descriptors for the classification of hand shapes. As always found in literature, such recognition systems consist of two phases: training and recognition. In our new practicalapproach, following the chosen appearance-based model, training and recognition is done in an interactive supervised way: the adaptation for untrained gestures is also solved by hand signals. Our experimental results with three different users are reported. In this paper, besides describing the recognition itself, we demonstrate our interactive training method in a practicalapplication.
Year
DOI
Venue
2002
10.1109/ICPR.2002.1048206
ICPR (3)
Keywords
Field
DocType
appearance-based recognition,interactive training method,hand gesture recognition system,human computer interaction purpose,recognition system,different user,hand shape,fourier descriptors,supervised training,appearance-based model,hand signal,image classification,learning artificial intelligence,shape,shape analysis,human computer interaction,image processing,gesture recognition,automation,application software,control systems,virtual environment,computer vision
Computer science,Gesture,Gesture recognition,Feature (machine learning),Artificial intelligence,Contextual image classification,Computer vision,Pattern recognition,Three-dimensional face recognition,Intelligent character recognition,Speech recognition,Sketch recognition,Shape analysis (digital geometry)
Conference
Volume
ISSN
ISBN
3
1051-4651
0-7695-1695-X
Citations 
PageRank 
References 
2
0.43
2
Authors
2
Name
Order
Citations
PageRank
Attila Licsár1544.80
Sziranyi, T.239544.76