Comparing Feature Point Tracking with Dense Flow Tracking for Facial Expression Recognition

  • Authors:
  • José V. Ruiz;Belén Moreno;Juan José Pantrigo;Ángel Sánchez

  • Affiliations:
  • Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain 28933;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain 28933;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain 28933;Departamento de Ciencias de la Computación, Universidad Rey Juan Carlos, Madrid, Spain 28933

  • Venue:
  • IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
  • Year:
  • 2009

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Abstract

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.