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Active appearance model applied to emotion recognition through partially occluded facial expression

Grant number: 14/04020-9
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): May 01, 2014
Effective date (End): April 30, 2015
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Principal Investigator:Hélio Pedrini
Grantee:Flavio Altinier Maximiano da Silva
Host Institution: Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:11/22749-8 - Challenges in exploratory visualization of multidimensional data: paradigms, scalability and applications, AP.TEM


Facial expressions are an important demonstration of human's humours and emotions. Algorithms capable of recognizing facial expressions and associate them to emotions become useful for many applications and the Machine Learning approach shows to be efficient in solving this challenge, which becomes even more difficult when facial regions are occluded. An Active Appearance Model must be trained in order to reconstruct the occluded facial region - whose facial expression and related emotion were previously labeled in static images. Feature selection filters will be applied to these images and dimensionality reduction methods will be used to compress the data. This research project focuses on studying and implementing a robust Active Appearance Model to reconstruct occluded regions of the image and then verify its efficiency in a classifier trained with non-occluded images. Experimental results will be tested on various public databases to evaluate the method performance.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MAXIMIANO DA SILVA, FLAVIO ALTINIER; PEDRINI, HELIO. Effects of cultural characteristics on building an emotion classifier through facial expression analysis. JOURNAL OF ELECTRONIC IMAGING, v. 24, n. 2, . (14/04020-9, 11/22749-8)

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