Abstract
This chapter presents two algorithms for hand posture recognition, based on the computational model of the ventral stream of visual cortex. In the first algorithm, the hand postures are recognized by elastic graph matching between a model graph and patterns in an input image. Graph nodes are assigned to geometrically significant positions in a hand image and each node in the graph is labeled using an image feature extracted using the computational model of the ventral stream. A radial basis function is utilized as the similarity function for the matching process. In the second algorithm, shape based features which are good in interclass discrimination are identified. To classify the patterns, similarities of the shapes are compared. The second algorithm needs only single training image per class. The overall algorithm is computationally efficient due to the simplicity of the classification process. The algorithm is implemented in real-time for interaction between a human and a virtual character Handy. Handy can symbolically express the recognized hand posture and pronounce the relevant class name. The experimental results show that the algorithm is robust to hand posture size and shape variations and varying lighting conditions.
If the human brain were so simple that we could understand it, we would be so simple that we couldn’t
Emerson M. Pugh.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The feature, among a bunch of node features, which provides maximum similarity.
- 2.
The segmentation is also done in HSV color space. In comparison, segmentation in YCbCr color space provided better results.
References
C. Bishop, Neural Networks for Pattern Recognition (Oxford University Press, Oxford, 1995)
M. Goodale, A. Milner, Separate visual pathways for perception and action. Trends Neurosci. 15, 20–25 (1992)
M. Lades, J.C. Vorbruggen, J. Buhmann, J. Lange, C. Malsburg, R.P. Wurtz, W. Konen, Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42(3), 300–311 (1993)
P.K. Pisharady, Computational intelligence techniques in visual pattern recognition, Ph.D. thesis, National University of Singapore, 2011
P.K. Pisharady, Q.S.H. Stephanie, P. Vadakkepat, A.P. Loh, Hand posture recognition using neuro-biologically inspired features, in Trends in Intelligent Robotics: 15th Robot World Cup and Congress, FIRA 2010, Bangalore, India, September 15–19, 2010, Proceedings, vol. 103, pp. 290–297
P.K. Pisharady, P. Vadakkepat, A.P. Loh, Graph matching based hand posture recognition using neuro-biologically inspired features, in International Conference on Control, Automation, Robotics and Vision (ICARCV) 2010 (Singapore), Dec 2010
P.K. Pisharady, P. Vadakkepat, A.P. Loh, Hand posture and face recognition using a fuzzy-rough approach, Int. J. Humanoid Rob. 07(03), 331–356 (2010)
M. Riesenhuber, T. Poggio, Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019–1025 (1999)
T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, T. Poggio, Robust object recognition with cortex-like mechanisms. IEEE Trans. Pattern Anal. Mach. Intell. 29(3), 411–426 (2007)
T. Serre, L. Wolf, T. Poggio, Object recognition with features inspired by visual cortex, in Conference on Computer Vision and Pattern Recognition, pp. 994–1000, San Diego, 2005, ed. by C. Schmid, S. Soatto, C. Tomasi
J. Triesch, C. Malsburg, Sebastien marcel hand posture and gesture datasets: Jochen triesch static hand posture database (1996), http://www.idiap.ch/resource/gestures/
J. Triesch, C. Malsburg, A system for person-independent hand posture recognition against complex backgrounds. IEEE Trans. Pattern Anal. Mach. Intell. 23(12), 1449–1453 (2001)
J. Triesch, C. Malsburg, Robust classification of hand postures against complex backgrounds, in Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, (Killington, VT, USA), pp. 170–175, Oct 1996
L. Wiskott, J.M. Fellous, N. Kruger, C. Malsburg, Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal Mach. Intell. 19(7), 775–779 (1997)
Acknowledgments
The figures and tables in Sect. 7.3 of this chapter are adapted from ‘Hand posture recognition using neuro-biologically inspired features’, Pramod Kumar Pisharady, Stephanie Quek Shu Hui, Prahlad Vadakkepat, and Loh Ai Poh, Trends in Intelligent Robotics, Springer, Book Series: Communications in Computer and Information Science, Vol.103, Page Nos. 290-297, Copyright @ 2010, with permission from Springer Science+Business Media.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media Singapore
About this chapter
Cite this chapter
Pisharady, P.K., Vadakkepat, P., Poh, L.A. (2014). Hand Posture Recognition Using Neuro-Biologically Inspired Features. In: Computational Intelligence in Multi-Feature Visual Pattern Recognition. Studies in Computational Intelligence, vol 556. Springer, Singapore. https://doi.org/10.1007/978-981-287-056-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-287-056-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-287-055-1
Online ISBN: 978-981-287-056-8
eBook Packages: EngineeringEngineering (R0)