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    Accelerated face detector training using the PSL framework

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    Submitted_SusnjakBarczakHawick2009_RLIMS.pdf (153.9Kb)
    Abstract
    We train a face detection system using the PSL framework [1] which combines the AdaBoost learning algorithm and Haar-like features. We demonstrate the ability of this framework to overcome some of the challenges inherent in training classifiers that are structured in cascades of boosted ensembles (CoBE). The PSL classifiers are compared to the Viola-Jones type cas- caded classifiers. We establish the ability of the PSL framework to produce classifiers in a complex domain in significantly reduced time frame. They also comprise of fewer boosted en- sembles albeit at a price of increased false detection rates on our test dataset. We also report on results from a more diverse number of experiments carried out on the PSL framework in order to shed more insight into the effects of variations in its adjustable training parameters.
    Citation
    Susnjak, T., Barczak, A.L.C., Hawick, K.A. (2009), Accelerated face detector training using the PSL framework, Research Letters in the Information and Mathematical Sciences, 13, 68-80
    Date
    2009
    Author
    Susnjak, T.
    Barczak, A.L.C.
    Hawick, K.A.
    Publisher
    Massey University
    URI
    http://hdl.handle.net/10179/4504
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    • Research Letters in the Information and Mathematical Sciences
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    Copyright © 2018 Massey University
    Contact Us | Send Feedback | Copyright Take Down Request
    DSpace software copyright © Duraspace
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