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    A novel multispectral and 2.5D/3D image fusion camera system for enhanced face recognition

    Williams, William (2017) A novel multispectral and 2.5D/3D image fusion camera system for enhanced face recognition. Doctoral thesis, Birkbeck, University of London.

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    Abstract

    The fusion of images from the visible and long-wave infrared (thermal) portions of the spectrum produces images that have improved face recognition performance under varying lighting conditions. This is because long-wave infrared images are the result of emitted, rather than reflected, light and are therefore less sensitive to changes in ambient light. Similarly, 3D and 2.5D images have also improved face recognition under varying pose and lighting. The opacity of glass to long-wave infrared light, however, means that the presence of eyeglasses in a face image reduces the recognition performance. This thesis presents the design and performance evaluation of a novel camera system which is capable of capturing spatially registered visible, near-infrared, long-wave infrared and 2.5D depth video images via a common optical path requiring no spatial registration between sensors beyond scaling for differences in sensor sizes. Experiments using a range of established face recognition methods and multi-class SVM classifiers show that the fused output from our camera system not only outperforms the single modality images for face recognition, but that the adaptive fusion methods used produce consistent increases in recognition accuracy under varying pose, lighting and with the presence of eyeglasses.

    Metadata

    Item Type: Thesis
    Copyright Holders: The copyright of this thesis rests with the author, who asserts his/her right to be known as such according to the Copyright Designs and Patents Act 1988. No dealing with the thesis contrary to the copyright or moral rights of the author is permitted.
    Divisions: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Acquisitions And Metadata
    Date Deposited: 18 Aug 2017 11:11
    Last Modified: 13 Aug 2020 09:39
    URI: https://eprints.bbk.ac.uk/id/eprint/40272

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