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Facial expression recognition based on image pyramid and single-branch decision tree

Authors
  • Ashir, Abubakar M.1
  • Eleyan, Alaa2
  • 1 Selçuk University, Department of Electric and Electronic Engineering, Konya, Turkey , Konya (Turkey)
  • 2 Avrasya University, Department of Electric and Electronic Engineering, Trabzon, Turkey , Trabzon (Turkey)
Type
Published Article
Journal
Signal, Image and Video Processing
Publisher
Springer London
Publication Date
Jan 10, 2017
Volume
11
Issue
6
Pages
1017–1024
Identifiers
DOI: 10.1007/s11760-016-1052-9
Source
Springer Nature
Keywords
License
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Abstract

In this paper, a new approach has been proposed for improved facial expression recognition. The new approach is inspired by the compressive sensing theory and multiresolution approach to facial expression problems. Initially, each image sample is decomposed into desired pyramid levels at different sizes and resolutions. Pyramid features at all levels are concatenated to form a pyramid feature vector. The vectors are further reinforced and reduced in dimension using a measurement matrix based on compressive sensing theory. For classification, a multilevel classification approach based on single-branch decision tree has been proposed. The proposed multilevel classification approach trains a number of binary support vector machines equal to the number of classes in the datasets. Class of test data is evaluated through the nodes of the tree from the root to its apex. The results obtained from the approach are impressive and outperform most of its counterparts in the literature under the same databases and settings.

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