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Face Detection using Local SMQT Features and Split Up SNoW Classifier
Responsible organisation
2007 (English)Conference paper, Published paper (Refereed) Published
Abstract [en]

The purpose of this paper is threefold: firstly, the local Successive Mean Quantization Transform features are proposed for illumination and sensor insensitive operation in object recognition. Secondly, a split up Sparse Network of Winnows is presented to speed up the original classifier. Finally, the features and classifier are combined for the task of frontal face detection. Detection results are presented for the MIT+CMU and the BioID databases. With regard to this face detector, the Receiver Operation Characteristics curve for the BioID database yields the best published result. The result for the CMU+MIT database is comparable to state-of-the-art face detectors. A Matlab version of the face detection algorithm can be downloaded from http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=13701&o bjectType=FILE

Place, publisher, year, edition, pages
Honolulu, 2007.
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-9269ISI: 000248908100148Local ID: oai:bth.se:forskinfo367C46CDCF754D17C12572CF002EAC41OAI: oai:DiVA.org:bth-9269DiVA: diva2:837051
Conference
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Available from: 2012-09-18 Created: 2007-05-02 Last updated: 2015-06-30Bibliographically approved

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fulltext(127 kB)186 downloads
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f6b5e29c0bacbcf541bdf5db83fa8154217aafb9cef76e5786997ffe57e12457f862d93c756cd3581dfef7bf49c29bdfc3e6bab750044f867253c43e44c67710
Type fulltextMimetype application/pdf

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Nilsson, MikaelClaesson, Ingvar
Signal Processing

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf