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

Muhammad Tahir

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

19 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProceedings of the 18th IEEE International Conference on Image Processing (ICIP), 2011
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages765-768
Number of pages3708
ISBN (Print)978-1457713040
DOIs
Publication statusPublished - Sep 2011
Event18th IEEE International Conference on Image Processing (ICIP), 2011 - Brussels, Belgium
Duration: 1 Sep 2011 → …
http://www.icip2011.org/

Conference

Conference18th IEEE International Conference on Image Processing (ICIP), 2011
Period1/09/11 → …
Internet address

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