Open or Closed Mouth State Detection: Static Supervised Classification Based on Log-polar Signature

Christian Bouvier 1 Alexandre Benoit 1 Alice Caplier 1 Pierre-Yves Coulon 1
GIPSA-DIS - Département Images et Signal
Abstract : The detection of the state open or closed of mouth is an important information in many applications such as hypo-vigilance analysis, face features segmentation or emotions recognition. In this work we propose a supervised classification method for mouth state detection based on retina filtering and cortex analysis inspired by the human visual system. The first stage of the method is the learning of reference signatures (Log Polar Spectrums) from some open and closed mouth images manually classified. The signatures are constructed by computing the amplitude log-polar spectrum of the retina filtered images. Principal Components Analysis (PCA) is then performed using the Log Polar Spectrum as feature vectors to reduce the number of dimension by keeping 95 % of the total variance. Finally a binary SVM classifier is trained using the projections the principal components given by the PCA in order to classify the mouth.
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Submitted on : Tuesday, March 31, 2009 - 1:51:24 PM
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Christian Bouvier, Alexandre Benoit, Alice Caplier, Pierre-Yves Coulon. Open or Closed Mouth State Detection: Static Supervised Classification Based on Log-polar Signature. Advanced Concepts for Intelligent Vision Systems 2008, Oct 2008, Juan-les-Pins, France. pp.1093-1102. ⟨hal-00372148⟩



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