Title:
On the Design of Cascades of Boosted Ensembles for Face Detection

dc.contributor.author Brubaker, S. Charles
dc.contributor.author Wu, Jianxin
dc.contributor.author Sun, Jie
dc.contributor.author Mullin, Matthew D.
dc.contributor.author Rehg, James M.
dc.date.accessioned 2006-03-17T14:54:18Z
dc.date.available 2006-03-17T14:54:18Z
dc.date.issued 2005
dc.description.abstract Cascades of boosted ensembles have become popular in the object detection community following their highly successful introduction in the face detector of Viola and Jones. Since then, researchers have sought to improve upon the original approach by incorporating new methods along a variety of axes (e.g. alternative boosting methods, feature sets, etc). We explore several axes that have not yet received adequate attention in this context: cascade learning, stronger weak hypotheses, and feature filtering. We present a novel strategy to determine the appropriate balance between false positive and detection rates in the individual stages of the cascade, enabling us to control our experiments to a degree not previously possible. We show that while the choice of boosting method has little impact on the detector's performance and feature filtering is largely ineffective, the use of stronger weak hypotheses based on CART classifiers can significantly improve upon the standard results. en
dc.format.extent 495405 bytes
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/8365
dc.language.iso en_US en
dc.publisher Georgia Institute of Technology en
dc.relation.ispartofseries GVU Technical Report;GIT-GVU-05-28 en
dc.subject Face detection en
dc.subject Cascade en
dc.subject Cascade learning en
dc.title On the Design of Cascades of Boosted Ensembles for Face Detection en
dc.type Text
dc.type.genre Technical Report
dspace.entity.type Publication
local.contributor.author Rehg, James M.
local.contributor.corporatename GVU Center
local.relation.ispartofseries GVU Technical Report Series
relation.isAuthorOfPublication af5b46ec-ffe2-4ce4-8722-1373c9b74a37
relation.isOrgUnitOfPublication d5666874-cf8d-45f6-8017-3781c955500f
relation.isSeriesOfPublication a13d1649-8f8b-4a59-9dec-d602fa26bc32
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