- Research Article
- Open Access
Data Fusion Boosted Face Recognition Based on Probability Distribution Functions in Different Colour Channels
EURASIP Journal on Advances in Signal Processing volume 2009, Article number: 482585 (2009)
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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Demirel, H., Anbarjafari, G. Data Fusion Boosted Face Recognition Based on Probability Distribution Functions in Different Colour Channels. EURASIP J. Adv. Signal Process. 2009, 482585 (2009). https://doi.org/10.1155/2009/482585
- Face Image
- Probability Distribution Function
- Data Fusion
- Colour Channel
- Fusion Technique