Mixture models with skin and shadow probabilities for fingertip input applications

Chih Chang Yu, Hsu Yung Cheng, Chien Cheng Lee

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


This paper proposes an accurate moving skin region detection method for video-based human-computer interface using gestures or fingertips. Using Gaussian mixture models as groundwork, the proposed method expresses the features of skins in a probability form and incorporates them into the mixture-based framework. Moreover, to alleviate the influence of shadows, the properties of shadows are also formulated as probabilities and used for shadow detection and elimination. In addition to moving skin region detection, this paper also develops two practical fingertip input applications to demonstrate the accuracy of the proposed detection method. The two applications are Mandarin Phonetic Symbol combination recognition system and single fingertip virtual keyboard implementation. Experimental results have shown the advantages of the proposed detection method and the effectiveness of the two application implementations.

Original languageEnglish
Pages (from-to)819-828
Number of pages10
JournalJournal of Visual Communication and Image Representation
Issue number7
StatePublished - 2013


  • Feature extraction
  • Human-computer interface
  • Mandarin phonetic symbol
  • Mixture models
  • Moving skin region detection
  • Shadow detection
  • Video-based fingertip input
  • Virtual touch keyboard


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