@inproceedings{bd38d60af6674ec1b586846b3da8aeab,
title = "Facial expression analysis for emotion recognition using kernel methods and statistical models",
abstract = "In this paper we present our framework for facial expression analysis using static models and kernel methods for classification. We describe the characterization methodology from parametric model. Also quantitatively evaluated the accuracy for feature detection and estimation of the parameters associated with facial expressions, analyzing its robustness to variations in pose. Then, a methodology of emotion characterization is introduced to perform the recognition. Furthermore, a cascade classifiers using kernel methods it is performed for emotion recognition. The experimental results show that the proposed model can effectively detect the different facial expressions. The model used and characterization methodology showed efficient to detect the emotion type in 93.4\% of the cases.",
keywords = "Emotion Recognition, Facial expression, Facial Features, Kernel Methods, Statistical Models",
author = "Garcia, \{Hern{\'a}n F.\} and Torres, \{Cristian A.\} and Hurtado, \{Jorge Ivan Marin\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014 ; Conference date: 17-09-2014 Through 19-09-2014",
year = "2015",
month = jan,
day = "14",
doi = "10.1109/STSIVA.2014.7010188",
language = "English",
series = "2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2014 19th Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2014",
address = "United States",
}