Data Fusion Boosted Face Recognition Based on Probability Distribution Functions in Different Colour Channels. (28th June 2009)

• Record Type: Journal Article
• Authors: Demirel Demirel, Hasan Hasan; Anbarjafari Anbarjafari, Gholamreza Gholamreza
• Other Names: Dharanipragada Dharanipragada Satya Satya Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2009(2009)
• Publication Date: 2009-06-28
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2009/482585
• Languages: English
• ISSNs: 1687-6172

Abstract

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.