Paper Titles

Venture to Achieve Automated Mapping with ArcGIS-Based MESIS
p.3034

An Enhanced Cross-Layer MAC Design for Energy Efficiency in Wireless Sensor Networks
p.3038

A Comparative Analysis of Shipping Emissions Estimation Methodologies and their Applicability
p.3042

A Fault-Tolerant Data Center Network Structure
p.3048

A Gesture Recognition Algorithm Based on PCA and BP Neural Network
p.3053

A New Piecewise Rational Reparameterization Method
p.3057

A Novel Algorithm for Classifying Measuring Curves Based on Interval Estimation
p.3061

A Procedure to Evaluate the Indoor Global Quality by a Sub Objective-Objective Procedure
p.3065

A Target Selection Algorithm of Seeker Based on the Prior Information and SVM
p.3071

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 734-737A Gesture Recognition Algorithm Based on PCA and...

A Gesture Recognition Algorithm Based on PCA and BP Neural Network

Article Preview

Abstract:

Gesture recognition has many applications in fields such as the intelligent robot, human computer interaction and so on. The classical BP neural network has its advantages in modeling the highly nonlinear mapping from features to gesture meanings, and could avoid hard-coded feature extraction. However, it usually takes a rather long training and testing time, especially in dealing with redundant high dimensional data. To address this drawback, we combine the BP neural network and PCA, and propose an improved algorithm. Experiments demonstrate the feasibility and efficiency of the proposed algorithm by comparing with the classical one.

You might also be interested in these eBooks

Resources and Sustainable Development

Info:

Periodical:

Advanced Materials Research (Volumes 734-737)

Pages:

3053-3056

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.734-737.3053

Citation:

Cite this paper

Online since:

August 2013

Authors:

Hong Yi Li, Jun Jie Chen, Xin Li, Di Zhao

Keywords:

BP Neural Network (BPNN), Gesture Recognition, Principal Component Analysis (PCA)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] B.G. V. Kumar and R. Aravind: Computationally efficient algorithm for face super-resolution using (2D)2-PCA based prior, IET Image Process, Vol. 4, No. 2 (2010), p.61–69.

DOI: 10.1049/iet-ipr.2009.0072

[2] F. Yang, H. Lu, W. Zhang, G. Yang: Visual tracking via bag of features, IET Image Process, Vol. 6, No. 2 (2012), p.115–128.

DOI: 10.1049/iet-ipr.2010.0127

[3] G. Yang, X. Xi, Y. Yin: Finger-vein Recognition Based on (2D)2 PCA and Metric Learning, Journal of Biomedicine and Biotechnology, Vol. 2012 (2012), Article ID 324249.

DOI: 10.1155/2012/324249

[4] H.S. Wang, Y.N. Wang, Y.C. Wang: Cost estimation of plastic injection molding parts through integration of PSO and BP neural network, Expert Systems with Applications, Vol. 40 (2013), p.418–428.

DOI: 10.1016/j.eswa.2012.01.166

[5] H. Wang, Y. Leng, Z. Wang, X. Wu: Application of image correction and bit-plane fusion in generalized PCA based face recognition, Pattern Recognition Letters, Vol. 28, No. 16 (2007), pp.2532-2358.

DOI: 10.1016/j.patrec.2007.07.015

[6] S. Osowski, S. Lukasiewicz: PCA transformation and Support Vector Machine for recognition of the noisy images, PRZEGLAD ELEKTROTECHNICZNY, Vol. 88, No. 3A (2012), pp.4-6.

[7] Y. He, Y. Tan, Y. Sun: Wavelet neural network approach for fault diagnosis of analogue circuits, in Proc. Circuits Devices Syst., Vol. 151, No. 4 (2004), p.379–384.

DOI: 10.1049/ip-cds:20040495

[8] Y. W. Pang, J.P. Deng, Y. Yuan: Incremental threshold learning for classifier selection, NEUROCOMPUTING, Vol. 89 (2012), pp.89-95.

DOI: 10.1016/j.neucom.2012.01.012

[9] Z. Zhang, C. Wang: The Research of Vehicle Plate Recognition Technical Based on BP Neural Network, AASRI Procedia, Vol. 1 (2012), pp.74-81.

DOI: 10.1016/j.aasri.2012.06.013

[10] M. Hasanuzzaman, T. Zhang, V. Ampornaramveth, H. Gotoda, Y. Shirai, H. Ueno: Adaptive visual gesture recognition for human–robot interaction using a knowledge-based software platform, Robotics and Autonomous Systems, Vol. 55, No. 8 (2007).

DOI: 10.1016/j.robot.2007.03.002

[11] Y. Zhu, G. Xu, D. J. Kriegman: A Real-Time Approach to the Spotting, Representation, and Recognition of Hand Gestures for Human–Computer Interaction, Computer Vision and Image Understanding, Vol. 85, No. 3 (2002), pp.189-208.

DOI: 10.1006/cviu.2002.0967

[12] J. Liu, L. Zhong, J. Wickramasuriya, V. Vasudevan: uWave: Accelerometer-based personalized gesture recognition and its applications, Pervasive and Mobile Computing, Vol. 5, No. 6 (2009), pp.657-675.

DOI: 10.1016/j.pmcj.2009.07.007

[13] M. C. Shin, L. V. Tsap, D. B. Goldgof: Gesture recognition using Bezier curves for visualization navigation from registered 3-D data, Pattern Recognition, Vol. 37, No. 5 (2004), pp.1011-1024.

DOI: 10.1016/j.patcog.2003.11.007

[14] F. Chen, C. Fu, C. Huang: Hand gesture recognition using a real-time tracking method and hidden Markov models, Image and Vision Computing, Vol. 21, No. 8 (2003), pp.745-758.

DOI: 10.1016/s0262-8856(03)00070-2

[15] Chung-Lin Huang, Ming-Shan Wu, Sheng-Hung Jeng: Gesture recognition using the multi-PDM method and hidden Markov model, Image and Vision Computing, Vol. 18, No. 11 (2000), pp.865-879.

DOI: 10.1016/s0262-8856(99)00042-6