Open Access

Data Fusion Boosted Face Recognition Based on Probability Distribution Functions in Different Colour Channels

EURASIP Journal on Advances in Signal Processing20092009:482585

https://doi.org/10.1155/2009/482585

Received: 20 November 2008

Accepted: 20 May 2009

Published: 28 June 2009

Abstract

A new and high performance face recognition system based on combining the decision obtained from the probability distribution functions (PDFs) of pixels in different colour channels is proposed. The PDFs of the equalized and segmented face images are used as statistical feature vectors for the recognition of faces by minimizing the Kullback-Leibler Divergence (KLD) between the PDF of a given face and the PDFs of faces in the database. Many data fusion techniques such as median rule, sum rule, max rule, product rule, and majority voting and also feature vector fusion as a source fusion technique have been employed to improve the recognition performance. The proposed system has been tested on the FERET, the Head Pose, the Essex University, and the Georgia Tech University face databases. The superiority of the proposed system has been shown by comparing it with the state-of-art face recognition systems.

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Authors’ Affiliations

(1)
Department of Electrical and Electronic Engineering, Eastern Mediterranean University

Copyright

© H. Demirel and G. Anbarjafari. 2009

This article is published under license to BioMed Central Ltd. 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.