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  • Likelihood normalization for face authentication in variable recording conditions

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    20504_1.pdf (366.1Kb)
    Author(s)
    Sanderson, C
    Paliwal, KK
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2002
    Metadata
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    Abstract
    In this paper we evaluate the effectiveness of two likelihood normalization techniques, the background model set (BMS) and the universal background model (UBM), for improving performance and robustness of four face authentication systems utilizing a Gaussian mixture model (GMM) classifier. The systems differ in the feature extraction method used: eigenfaces (PCA), 2-D DCT, 2-D Gabor wavelets and DCT-mod2. Experiments on the VidTIMIT database, using test images corrupted either by an illumination change or compression artefacts, suggest that likelihood normalization has little effect when using PCA derived features, while ...
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    In this paper we evaluate the effectiveness of two likelihood normalization techniques, the background model set (BMS) and the universal background model (UBM), for improving performance and robustness of four face authentication systems utilizing a Gaussian mixture model (GMM) classifier. The systems differ in the feature extraction method used: eigenfaces (PCA), 2-D DCT, 2-D Gabor wavelets and DCT-mod2. Experiments on the VidTIMIT database, using test images corrupted either by an illumination change or compression artefacts, suggest that likelihood normalization has little effect when using PCA derived features, while providing significant performance improvements when using the remaining features.
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    Conference Title
    2002 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL I, PROCEEDINGS
    Volume
    1
    Publisher URI
    http://ieeexplore.ieee.org/servlet/opac?punumber=8052
    DOI
    https://doi.org/10.1109/ICIP.2002.1038019
    Copyright Statement
    © 2002 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Publication URI
    http://hdl.handle.net/10072/1490
    Collection
    • Conference outputs

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