The week's pick
Random Articles
Reseach Article
Dynamic Hand Gesture Recognition using PCA, Pruning and ANN
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 74 - Number 2 |
| Year of Publication: 2013 |
| Authors: S. M. Shitole, S. B. Patil, S. P. Narote |
10.5120/12859-9594
|
S. M. Shitole, S. B. Patil, S. P. Narote . Dynamic Hand Gesture Recognition using PCA, Pruning and ANN. International Journal of Computer Applications. 74, 2 ( July 2013), 24-29. DOI=10.5120/12859-9594
Abstract
Gesture recognition has the potential to be a natural and powerful tool supporting efficient and intuitive interaction between the human and the computer. The primary goal of hand gesture recognition research is to create a system which can identify specific hand gestures and use them to convey information for device control. This paper proposes a vision-based dynamic hand gesture recognition system using PCA, pruning and ANN. The system will find hand location in each frame. The hand segmentation is done using color marker for PCA. Skin color detection method is used for pruning and ANN. After connecting all these hand locations, the template is generated at the end of Segmentation. Template is used to extract the feature for training and testing purpose. The implementation uses the eigenspace which is determined by processing the eigenvalues and eigenvectors of the image set. The experimental results show ANN method gives more recognition rate than PCA and pruning.
References
- Vladimir I. Pavlovic, Rajeev Sharma, Thomas S. Huang, "Visual Interpretation of Hand Gestures for Human-Computer Interaction: A Review", IEEE Trans. Pattern analysis and machine intelligence, vol. 19, pp. 522-536, July 1997.
- Ali Erol, George Bebis, Mircea Nicolescu, Richard D. Boyle, Xander Twombly, "Vision-based hand pose estimation: A review", Computer Vision and Image Understanding 108 (2007) 52–73.
- M. W. Krueger, Artificial Reality II, Addison-Wesley, 1991.
- Zhi-gang Xu, Hong-lei Zhu, "Vision-based Detection of Dynamic Gesture", IEEE International Conference on Test and Measurement, 2009.
- Hwei-Jen Lin, Shu-Yi Wang, Shwu-Huey Yen, and Yang-Ta Kao," Face Detection Based on Skin Color Segmentation and Neural Network", International conference on Neural networks and brain, 2005, vol. 2, pp. 1144-1149.
- J. Alon, V. Athitsos, Q. Yuan, S. Sclaroff, "A Unified Framework for gesture recognition and spatiotemporal gesture segmentation" Ieee Transactions On Pattern Analysis And Machine Intelligence, vol. 31, no. 9, september 2009, pp. 1685-1699.
- Mahmoud M. Zaki and Samir I. Shaheen, "Sign language recognition using a combination of new vision based features", Pattern Recognition Letters 32 (2011), pp. 572–577.
- Sushmita Mitra and Tinku Acharya, "Gesture Recognation : A Survey", IEEE trans. Systems, man, and cybernetics—part c: applications and reviews, vol. 37, may 2007.
- Psarrou, A. , Gong, S. , Walter, M. ,. "Recognition of human gestures and behavior based on motion trajectories". Image Vision Comput. 2002, vol. 20, 349–358.
- M. Masudur Rahman, Seiji Ishikawa, "Human motion recognition using an eigenspace", Pattern Recognition Letters 26 (2005) 687–697
- Rybach, D. , 2006. Appearance-Based Features for Automatic Continuous Sign Language Recognition. Diploma Thesis, RWTH Aachen University, Aachen, Germany.
- Solomon Raju Kota, J. L. Raheja, Ashutosh Gupta, Archana Rathi, Shashikant Sharma, "Principal Component Analysis for Gesture Recognition using SystemC ", International Conference on Advances in Recent Technologies in Communication and Computing,2009,pp. 732-737.
- Md. R. Ahsan, Muhammad I. Ibrahimy, and Othman O. Khalifa "Hand motion detection from EMG signals by using ANN based classifier for Human Computer Interaction", Modeling, Simulation and Applied Optimization (ICMSAO), 2011 4th International Conference on 2011, pp. 1 – 6.
Index Terms
Keywords