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National Academy of Sciences, Proceedings of the National Academy of Sciences, 24(112), p. 7472-7477, 2015

DOI: 10.1073/pnas.1423147112

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An estimate of the number of tropical tree species

Journal article published in 2015 by J. W. Ferry Slik, J. W. Ferry Slik, Víctor Arroyo-Rodríguez ORCID, Shin-Ichiro Aiba, Patricia Alvarez-Loayza, Luciana F. Alves ORCID, Peter Ashton, Patricia Balvanera, Meredith L. Bastian, Peter J. Bellingham, Eduardo van den Berg ORCID, Luis Bernacci, Polyanna da Conceição Bispo ORCID, Lilian Blanc, Katrin Böhning-Gaese and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

Facial emotions are the most expressive way to display emotions. Many algorithms have been proposed which employ a particular set of people (usually a database) to both train and test their model. This paper focuses on the challenging task of database independent emotion recognition, which is a generalized case of subject-independent emotion recognition. The emotion recognition system employed in this work is a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). McFIS has two components, a neuro-fuzzy inference system, which is the cognitive component and a self-regulatory learning mechanism, which is the meta-cognitive component. The meta-cognitive component, monitors the knowledge in the neuro-fuzzy inference system and decides on what-to-learn, when-to-learn and how-to-learn the training samples, efficiently. For each sample, the McFIS decides whether to delete the sample without being learnt, use it to add/prune or update the network parameter or reserve it for future use. This helps the network avoid over-training and as a result improve its generalization performance over untrained databases. In this study, we extract pixel based emotion features from well-known (Japanese Female Facial Expression) JAFFE and (Taiwanese Female Expression Image) TFEID database. Two sets of experiment are conducted. First, we study the individual performance of both databases on McFIS based on 5-fold cross validation study. Next, in order to study the generalization performance, McFIS trained on JAFFE database is tested on TFEID and vice-versa. The performance The performance comparison in both experiments against SVNI classifier gives promising results.