Paper Info
Title
Feature selection based on co-clustering for effective facial expression recognition
Abstract
Facial expressions are considered to be an effective and non-verbal means of expressing the emotional states of humans in more natural and non-intrusive way. Automatically recognizing the emotions consequently contributes towards the advances in the areas such as human computer interaction, clinical psychology and data-driven animations. Deriving a relevant and reduced set of features is a vital step for effective facial expression recognition. In this paper, we propose a co-clustering based approach to the selection of distinguished and interpretable features to deal with the curse of dimensionality issue. First, the features are extracted from images using a bank of Gabor filters. Then, a co-clustering based algorithm is designed to seek distinguishable features based on their non-inclusive information in co-clusters. Experiments illustrate that the selected features are accurate and effective for the facial expression recognition on JAFFE database and the best recognition rate is obtained by using selected features with SVM for classification. Moreover, we illustrate that the selected features not only reduces the dimensionality but also identify the distinguishable face regions on images amongst all expressions.
Year
DOI
Venue
2016
10.1109/ICMLC.2016.7860876
2016 International Conference on Machine Learning and Cybernetics (ICMLC)
Keywords
Field
DocType
Face Expression Recognition,Co-clustering,Gabor Wavelets,Feature Selection
Facial recognition system,Three-dimensional face recognition,Feature selection,Pattern recognition,Expression (mathematics),Feature (computer vision),Computer science,Feature extraction,Facial expression,Feature (machine learning),Artificial intelligence,Machine learning
Conference
Volume
ISBN
Citations 
1
978-1-5090-0391-4
0
PageRank 
References 
Authors
0.34
4
4
Name
Order
Citations
PageRank
Sheheryar Khan163.11
Lijiang Chen230423.22
Xuefei Zhe310.70
Hong Yan43628335.04