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The Scientific World Journal
Volume 2014 (2014), Article ID 672630, 6 pages
Research Article

Kruskal-Wallis-Based Computationally Efficient Feature Selection for Face Recognition

1Department of Software Engineering, Foundation University, Rawalpindi 46000, Pakistan
2Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology Islamabad, Islamabad 44000, Pakistan
3Department of Computer Science and Software Engineering, International Islamic University, Islamabad 44000, Pakistan

Received 5 December 2013; Accepted 10 February 2014; Published 21 May 2014

Academic Editors: S. Balochian, V. Bhatnagar, and Y. Zhang

Copyright © 2014 Sajid Ali Khan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Face recognition in today’s technological world, and face recognition applications attain much more importance. Most of the existing work used frontal face images to classify face image. However these techniques fail when applied on real world face images. The proposed technique effectively extracts the prominent facial features. Most of the features are redundant and do not contribute to representing face. In order to eliminate those redundant features, computationally efficient algorithm is used to select the more discriminative face features. Extracted features are then passed to classification step. In the classification step, different classifiers are ensemble to enhance the recognition accuracy rate as single classifier is unable to achieve the high accuracy. Experiments are performed on standard face database images and results are compared with existing techniques.