Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling visual perception for image processing
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Mouth state estimation in mobile computing environment
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Nonlinear color space and spatiotemporal MRF for hierarchical segmentation of face features in video
IEEE Transactions on Image Processing
Robust visual speakingness detection using bi-level HMM
Pattern Recognition
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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.