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On the Expressivity of Alignment-Based Distance and Similarity Measures on Sequences and Trees in Inducing Orderings Read chapter Read first chapter

2013 | OriginalPaper | Chapter

Facial Expression Recognition Using Diffeomorphic Image Registration Framework

Authors : Bartlomiej W. Papiez, Bogdan J. Matuszewski, Lik-Kwan Shark, Wei Quan

Published in: Mathematical Methodologies in Pattern Recognition and Machine Learning

Publisher: Springer New York

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Abstract

This paper presents a new method for facial expression modelling and recognition based on diffeomorphic image registration parameterised via stationary velocity fields in the log-Euclidean framework. The validation and comparison are done using different statistical shape models (SSM) built using the Point Distribution Model (PDM), velocity fields and deformation fields. The obtained results show that the facial expression representation based on stationary velocity fields can be successfully utilised in facial expression recognition, and this parameterisation produces a higher recognition rate than the facial expression representation based on deformation fields.

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previous chapter A Robust Deformable Model for 3D Segmentation of the Left Ventricle from Ultrasound Data
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Metadata
Title
Facial Expression Recognition Using Diffeomorphic Image Registration Framework
Authors
Bartlomiej W. Papiez
Bogdan J. Matuszewski
Lik-Kwan Shark
Wei Quan
Copyright Year
2013
Publisher
Springer New York
Book
Mathematical Methodologies in Pattern Recognition and Machine Learning

Print ISBN: 978-1-4614-5075-7

Electronic ISBN: 978-1-4614-5076-4

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-1-4614-5076-4_12