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Kernel Grouped Multivariate Discriminant Analysis for Hyperspectral Image Classification Read chapter Read first chapter

2014 | OriginalPaper | Chapter

Semi-dynamic Facial Expression Recognition Based on Masked Displacement Image

Authors : Hamid Sadeghi, Abolghasem Asadollah Raie

Published in: Artificial Intelligence and Signal Processing

Publisher: Springer International Publishing

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Abstract

Facial expression recognition, as an interesting problem in pattern recognition and computer vision, has been performed by means of dynamic and static methods in recent years. Though dynamic information plays important role in facial expression recognition, utilizing the entire dynamic information of expression image sequences have high computational cost compared to the static cases. In order to reduce the computational cost, only neutral and emotional faces can be used instead of entire image sequence. In the previous research, this idea has been employed by means of DLBPHS method which vanish facial important small displacements by subtracting LBP features of neutral and emotional face images. In this paper, a novel semi-dynamic approach is proposed to utilize two face images by subtracting neutral image from emotional image. In this method, LBP features are extracted from masked difference image. In the masking procedure, eyes and mouth being important regions in facial expression recognition are selected for feature extraction step. Evaluation of the proposed algorithm on the standard databases shows a significant accuracy improvement compared to DLBPHS method.

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previous chapter Unconstrained Head Pose Estimation with Constrained Local Model and Memory Based Particle Filter by 3D Point Distribution Models
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Metadata
Title
Semi-dynamic Facial Expression Recognition Based on Masked Displacement Image
Authors
Hamid Sadeghi
Abolghasem Asadollah Raie
Copyright Year
2014
Book
Artificial Intelligence and Signal Processing

Print ISBN: 978-3-319-10848-3

Electronic ISBN: 978-3-319-10849-0

Copyright Year: 2014

DOI
https://doi.org/10.1007/978-3-319-10849-0_11

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