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Erschienen in:
On the Expressivity of Alignment-Based Distance and Similarity Measures on Sequences and Trees in Inducing Orderings Kapitel lesen Erstes Kapitel lesen

2013 | OriginalPaper | Buchkapitel

Facial Expression Recognition Using Diffeomorphic Image Registration Framework

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

Erschienen in: Mathematical Methodologies in Pattern Recognition and Machine Learning

Verlag: 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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Vorheriges Kapitel A Robust Deformable Model for 3D Segmentation of the Left Ventricle from Ultrasound Data
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Metadaten
Titel
Facial Expression Recognition Using Diffeomorphic Image Registration Framework
verfasst von
Bartlomiej W. Papiez
Bogdan J. Matuszewski
Lik-Kwan Shark
Wei Quan
Copyright-Jahr
2013
Verlag
Springer New York
Buch
Mathematical Methodologies in Pattern Recognition and Machine Learning

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

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

Copyright-Jahr: 2013

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

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