Learning to recognise talking faces
作者： J. Luettin , N.A. Thacker , S.W. Beet
关键词: Speech recognition 、 Face (geometry) 、 Facial recognition system 、 Artificial intelligence 、 Hidden Markov model 、 Identification (information) 、 Pattern recognition 、 Computer science 、 Parametric model 、 Visible Speech 、 Speech production 、 Sequence
摘要: An approach for person identification is described based on spatio-temporal analysis of the talking face. A represented by a parametric model visible speech articulators and their temporal characteristics during production. The consists shape parameters, representing lip contour intensity parameters grey level distribution in mouth region. used to track lips image sequences where are recovered from tracking results. While some these relate information, others intuitively related different persons we show that models features enable successful identification. We as mixtures Gaussians dependencies hidden Markov models. Identifying performed estimating likelihood each having generated observed sequence with highest chosen identified person.
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