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432 Nooritawati Md Tahir, Aini Hussain, Salina Abdul Samad, Hafizah Husain and Mohd Yusof Jamaluddin interesting and remarkable performance is as in experiment 1 that contributed the best classification rate in each category. As a result, the combination of E1, E2, E3 is chosen as the optimized eigenpostures. Conclusion In conclusion, a task of classifying four main human postures namely standing, sitting, bending and lying position based on eigenvectors analysis is presented. As can be seen from the experimental results, eigenspace technique based on statistical analysis prior to classification can be employed for human posture classification with high degree of accuracy. The initial thirty five feature vectors suggested by the rule of thumbs of PCA namely the KG rule, Scree Test and Cumulative Variance are trimmed down to a new subset of twenty feature vectors via the ANOVA. Further, the MCP and homogeneous subset tests have lessen the feature selection to only four eigenpostures and confirmed that the unseen postures have been correctly classified by using only three combination of eigenpostures as inputs to the neural network classifier. E1, E2, E3 are selected as the optimized combination. This suggests that the eigenspace technique along with statistical data analysis can be put into practice for posture recognition, which can lead to a wide variety of applications such as security systems, intruder’s alertness, gait analysis, action recognition, human computer interaction, action recognition for surveillance applications and tracking techniques for video coding, and image displays. The rules of thumb of PCA and statistical analysis have facilitated us to achieve the selection of eigenpostures for classification of human postures efficiently. Acknowledgement This work was supported by MOSTI under the IRPA Grant No: 03-02-02-0017-SR0003/07-03. The authors also acknowledge Prof Dr Burhanuddin Yeop Majlis as the Program Head and UiTM for the UiTM-JPA SLAB scholarship awards.
Feature Selection Based on Statistical Analysis 433 References [1] Ani1 K. Jain, Robert P.W. Duin, and Jianchang Mao (2000) Statistical Pattern Recognition: A Review. Proceeding of IEEE Transactions On Pattern Analysis And Machine Intelligence Vol. 22(1): 4-37. [2] Laveen Kanal (1974) Patterns in Pattern Recognition. Proceeding of IEEE Transactions on Information Theory, Vol. 20(6):697-722. [3] Nooritawati Md Tahir, Aini Hussain, Salina Abdul Samad, Hafizah Husain and Mohd Marzuki Mustafa (2006) Eigenposture for Classification. Journal of Applied Sciences 6(2):419-424. [4] Fekedulegn, B. Desta, Colbert, J.J and Hicks R.R. (2002). Coping with Multicollinearity: An Example on Application of Principal Components Regression in Dendroecology in Report of Department of Agriculture, Forest Service, Northeastern Research Station, Newton Square, USA. pp 2-24. [5] Walter J. Freeman (1987).Methods of Analysis of Brain Electrical & Magnetic Signals: Analytic Techniques Used in the Search for the Physiological Basis of the EEG, in EEG Handbook, Department of Molecular & Cell Biology, University of California, Berkeley, CA 94720, USA. [6] Lewis, A. (2001) Great Barrier Reef Depth and Elevation Model: GBRDEM. in Technical Report No. 33, Townsville, CRC Reef Research Centre Ltd. [7] http://lertap.curtin.edu.au/HTMLHelp/HTML/index.html?eigenvalues.html (1/2/2006). [8] IT Jolliffe (2002). Principal Component Analysis, Springer Series. pp 111-137. [9] Tom Mitchell (1997). Machine Learning, McGraw Hill. pp 81 -110. [10] Nooritawati Md Tahir, Aini Hussain, Salina Abdul Samad, Khairul Anuar Ishak and Rosmawati Abdul Halim (2006). Feature Selection for Classification Using Decision Tree in Proceeding of SCORed 2006, Malaysia. pp 99-102. [11] Smith L. (2002). A tutorial on Principal Components Analysis, Cornell University, USA, http://kybele.psych.cornell.edu/%7Eedelman/Psych-465-Spring-2003/PCA-tutorial.pdf, (6/01/2005). [12] Winston, P.H. (1992). Artificial Intelligence, 3rd Edition, Addison Wesley. pp 443-500. [13] Turk M. (2001). A Random Walk through Eigenspace. Special Issue on Machine Vision Applications, in IEICE Transaction Information and System. E84-D (12): 1586-1595. [14] Ji Q and X. Yang. (2002).Real Time 3D Face Pose Discrimination Based on Active IR Illumination. International Conference on Pattern Recognition, Japan, pp 310-313. [15] Ohba K., G. Clary and T. Tsukada (1998). Facial Expression Communication with FES. International Conference on Pattern Recognition, Japan, pp 1378-1381. [16] Algorri M.E. and A. Escobar (2004). Facial Gesture Recognition for Interactive Applications. IEEE Proceedings of Fifth Mexican International Conference in Computer Science, Mexico, pp 185-195. [17] Ozer B. and W. Wolf (2002). Real Time Posture and Activity Recognition. IEEE Proceedings on Motion and Video Computing, pp133-138.
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