Graph matching based hand posture recognition using neuro-biologically inspired features
Kumar P, P. Vadakkepat, P.
Poh, L.A.
Kumar P, P.
Citations
Altmetric:
Alternative Title
Abstract
An elastic graph matching algorithm using biologically inspired features is proposed for the recognition of hand postures. Each node in the graph is labeled using an image feature extracted using the computational model of the ventral stream of visual cortex. The graph nodes are assigned to geometrically significant positions in the hand image, and, the model graphs are created. Bunch graph method is used for modeling the variability in hand posture appearance. Recognition of a hand posture is done by the elastic graph matching between the model graphs and the input image. A radial basis function is used as the similarity function for the matching process. The proposed algorithm is tested on a 10 class hand posture database which consists of 478 grey scale images with light and dark backgrounds. The algorithm provided better recognition accuracy (96.35%) compared to the reported results (93.77%) in the literature. ©2010 IEEE.
Keywords
Biologically inspired features, Graph matching, Hand posture recognition, Human-computer interaction, Pattern recognition
Source Title
11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
Publisher
Series/Report No.
Collections
Rights
Date
2010
DOI
10.1109/ICARCV.2010.5707352
Type
Conference Paper