Accelerated face detector training using the PSL framework

dc.contributor.authorSusnjak, T.
dc.contributor.authorBarczak, A.L.C.
dc.contributor.authorHawick, K.A.
dc.date.accessioned2013-05-21T23:19:15Z
dc.date.available2013-05-21T23:19:15Z
dc.date.issued2009
dc.description.abstractWe 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.en
dc.identifier.citationSusnjak, 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, 68-80en
dc.identifier.issn1175-2777
dc.identifier.urihttp://hdl.handle.net/10179/4504
dc.language.isoenen
dc.publisherMassey Universityen
dc.subjectHaar-like featuresen
dc.subjectFace detectionen
dc.subjectAdaBoosten
dc.subjectCascades of boosted ensembles (CoBE)en
dc.subjectClassifiersen
dc.subjectViola-Jones detection
dc.titleAccelerated face detector training using the PSL frameworken
dc.typeArticleen
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