Face Recognition using Multi-Scale Local Phase Quantisation and Linear Regression Classifier

Tahir, Muhammad (2011) Face Recognition using Multi-Scale Local Phase Quantisation and Linear Regression Classifier. In: Proceedings of the 18th IEEE International Conference on Image Processing (ICIP), 2011. IEEE Computer Society, Piscataway, NJ, pp. 765-768. ISBN 978-1457713040

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Abstract

Linear Regression Classifier (LRC) is state-of-the-art face recognition method that represent a probe image as a linear combination of class specific models. However, this method views the image as a point in a feature space, and thus LRC cannot accommodate severe luminance alterations. Histogram-based features, such as Multiscale Local Phase Quantisation histogram (MLPQH) have gained reputation as powerful and attractive texture descriptors showing excellent results in terms of accuracy and computational complexity in face recognition. In this paper, MLPQH features are integrated with ``face'' features to confront the illumination problem in LRC. The main novelty is the fusion of histogram and face features using {\it z}-score normalisation and LRC classifier. The proposed system is evaluated on two benchmarks: ORL and Extended Yale B. The results indicate a significant increase in the performance when compared with state-of-the-art face recognition methods.

Item Type:	Book Section
Additional Information:	Paper presented at the 18th IEEE International Conference on Image Processing (ICIP), 2011 held in Brussels, Belgium 11 - 14 September 2011.
Uncontrolled Keywords:	face recognition, linear regression, multiscale local phase quantisation
Subjects:	G700 Artificial Intelligence
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Library Catalogue Organisation
Depositing User:	Muhammad Tahir
Date Deposited:	08 May 2012 10:51
Last Modified:	12 Oct 2019 22:29
URI:	http://nrl.northumbria.ac.uk/id/eprint/6802

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