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Spatio-temporal reasoning for reliable facial expression interpretation
Orozco, Francisco J.; García, Fabio A.; Arcos, Lluis; Gonzàlez, Jordi
Institut de Robòtica i Informàtica Industrial
Understanding human behaviours and emotions has received contributions from image analysis and pattern recognition techniques in order to tackle this challenge. The most popular facial expression classifiers deal with eyebrows and lips while avoiding eyelid motion. According to psychologists, eye motion is relevant for trust and deceit analysis as well for dichotomizing near facial expressions. Unlike previous approaches, we include the eyelid motion by constructing an appearance-based tracker (ABT). Subsequently, a Case-Based Reasoning (CBR) approach is applied by training a case-base with seven facial actions. We classify new facial expressions with respect to previous solutions, previously assessing confidence for the proposed solutions. Therefore, the proposed system yields efficient classification rates comparable to the best previous facial expression classifiers. The ABT and CBR combination provides trusty solutions by evaluating the confidence of the solution quality for eyebrows, mouth and eyes. Consequently, this method is robust and accurate for facial motion coding, and for confident classifications. The training is progressive, the quality of the solution increases with respect to previous solutions and do not need re-training processes.
Peer Reviewed
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la imatge i del senyal vídeo
Computer vision
Visió per ordinador
Classificació INSPEC::Pattern recognition::Computer vision
info:eu-repo/semantics/conferenceObject
Bielefeld University
         

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