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Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System

Subramanian, Kartick and Radhakrishnan, Venkatesh Babu and Ramasamy, Savitha (2014) Database Independent Human Emotion Recognition with Meta-Cognitive Neuro-Fuzzy Inference System. In: 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), APR 21-24, 2014, Singapore, SINGAPORE.

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Official URL: http://dx.doi.org/10.1073/pnas.1423147112

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.

Item Type: Conference Proceedings
Additional Information: Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Keywords: Emotion recognition; cross-dataset etacognition; neuro-fuzzy inference system; classification
Department/Centre: Division of Interdisciplinary Research > Supercomputer Education & Research Centre
Depositing User: Id for Latest eprints
Date Deposited: 31 Jul 2015 14:17
Last Modified: 31 Jul 2015 14:17
URI: http://eprints.iisc.ac.in/id/eprint/51977

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