Pose Invariant Face Recognition Using Probability Distribution Functions in Different Color Channels

In this letter a new and high performance pose invariant face recognition system based on the probability distribution functions (PDF) of pixels in different color channels is proposed. The PDFs of the equalized and segmented face images are used as statistical feature vectors for the recognition of...

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Bibliographic Details
Published in:IEEE signal processing letters 2008, Vol.15, p.537-540
Main Authors: Demirel, H., Anbarjafari, G.
Format: Article
Language:English
Subjects:
Face detection
Face recognition
feature vector fusion
Histograms
Image recognition
Image segmentation
Kullback-Leibler distance
Linear discriminant analysis
majority voting
Probability distribution
singular value decomposition
Skin
Spatial databases
Voting
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Summary:In this letter a new and high performance pose invariant face recognition system based on the probability distribution functions (PDF) of pixels in different color 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 KullbackLeibler distance (KLD) between the PDF of a given face and the PDFs of faces in the database. Feature vector fusion (FVF) and majority voting (MV) methods have been employed to combine feature vectors obtained from different color channels in HSI and YCbCr color spaces to improve the recognition performance. The proposed system has been tested on the FERET and the Head Pose face databases. The recognition rates obtained using FVF approach for FERET database is 98.00% compared with 94.60% and 68.80% for MV and principle component analysis (PCA)-based face recognition techniques, respectively.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2008.926729