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Human skin color detection in RGB space with Bayesian estimation of beta mixture models
KTH, School of Electrical Engineering (EES), Sound and Image Processing (Closed 130101).
KTH, School of Electrical Engineering (EES), Sound and Image Processing (Closed 130101).
2010 (English)In: EUSIPCO 2010, EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP , 2010, 1204-1208 p.Conference paper, Published paper (Refereed)
Abstract [en]

Human skin color detection plays an important role in the applicationsof skin segmentation, face recognition, and tracking. To builda robust human skin color classifier is an essential step. This paperpresents a classifier based on beta mixture models (BMM), whichuses the pixel values in RGB space as the features. We proposea Bayesian estimation method based on the variational inferenceframework to approximate the posterior distribution of the parametersin the BMM and take the posterior mean as a point estimateof the parameters. The well-known Compaq image database is usedto evaluate the performance of our BMM based classifier. Comparedto some other skin color detection methods, our BMM basedclassifier shows a better recognition performance.

Place, publisher, year, edition, pages
EUROPEAN ASSOC SIGNAL SPEECH & IMAGE PROCESSING-EURASIP , 2010. 1204-1208 p.
Series
TRITA-S3-SIP, ISSN 1652-4500
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering Computer Science
Research subject
SRA - ICT
Identifiers
URN: urn:nbn:se:kth:diva-34267ISI: 000349999100244Scopus ID: 2-s2.0-84863807861OAI: oai:DiVA.org:kth-34267DiVA: diva2:420032
Conference
18th European Signal Processing Conference (EUSIPCO 2010). Aalborg, Denmark. August 23-27, 2010
Note

QC 20111117

Available from: 2011-05-30 Created: 2011-05-30 Last updated: 2015-12-07Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • 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