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Erschienen in:
2009 | OriginalPaper | Buchkapitel
Comparing Feature Point Tracking with Dense Flow Tracking for Facial Expression Recognition
verfasst von : José V. Ruiz, Belén Moreno, Juan José Pantrigo, Ángel Sánchez
Erschienen in: Bioinspired Applications in Artificial and Natural Computation
Verlag: Springer Berlin Heidelberg
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This work describes a research which compares the facial expression recognition results of two point-based tracking approaches along the sequence of frames describing a facial expression: feature point tracking and holistic face dense flow tracking. Experiments were carried out using the Cohn-Kanade database for the six types of prototypic facial expressions under two different spatial resolutions of the frames (the original one and the images reduced to a 40% of its original size). Our experimental results showed that the dense flow tracking method provided in average for the considered types of expressions a better recognition rate (95.45% of success) than feature point flow tracking (91.41%) for the whole test set of facial expression sequences.