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Type: Artigo
Title: Geometrical Features And Active Appearance Model Applied To Facial Expression Recognition
Author: Maximiano da Silva
Flavio Altinier; Pedrini
Abstract: One of the most effective ways of expressing emotion is through facial expressions. This work proposes and discusses a geometrical descriptor based on the calculation of distances from coordinates of facial fiducial points, which are used as features for training support vector machines (SVM) to classify emotions. Three data sets are studied and six basic emotions are considered in our experiments. In comparison to other approaches available in the literature, the results obtained with our geometrical descriptor demonstrated to be very competitive, achieving high classification F-score rates. Additionally, we evaluate whether the combination of our geometrical descriptor with an appearance feature, the Gabor filter, allows emotions to be even more distinguishable for the classifier. The result is positive for two out of three data sets. Finally, to simulate in-the-wild scenarios, an active appearance model (AAM) is trained to position the fiducial points on the correct facial locations, instead of using the ones provided by the data sets. As the fitting error is considered acceptable, the former experiments are also conducted with the new data generated by the AAM. The results show a small drop on the F-score values when compared to the data originally provided by the data sets, but are still satisfactory.
Subject: Facial Expressions
Emotion Recognition
Active Appearance Model
Fiducial Points
Editor: World Scientific Publ CO PTE LTD
Rights: fechado
Identifier DOI: 10.1142/S0219467816500194
Date Issue: 2016
Appears in Collections:Unicamp - Artigos e Outros Documentos

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