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Spontaneous subtle expression recognition: Imbalanced databases and solutions

Version 2 2024-06-13, 12:43
Version 1 2019-05-09, 10:10
conference contribution
posted on 2024-06-13, 12:43 authored by AC Le Ngo, RCW Phan, J See
© Springer International Publishing Switzerland 2015. Facial expression analysis has been well studied in recent years; however, these mainly focus on domains of posed or clear facial expressions. Meanwhile, subtle/micro-expressions are rarely analyzed, due to three main difficulties: inter-class similarity (hardly discriminate facial expressions of two subtle emotional states from a person), intra-class dissimilarity (different facial morphology and behaviors of two subjects in one subtle emotion state), and imbalanced sample distribution for each class and subject. This paper aims to solve the last two problems by first employing preprocessing steps: facial registration, cropping and interpolation; and proposes a person-specific AdaBoost classifier with Selective Transfer Machine framework. While preprocessing techniques remove morphological facial differences, the proposed variant of AdaBoost deals with imbalanced characteristics of available subtle expression databases. Performance metrics obtained from experiments on the SMIC and CASME2 spontaneous subtle expression databases confirm that the proposed method improves classification of subtle emotions.

History

Volume

9006

Pagination

33-48

Location

Singapore

Start date

2014-11-01

End date

2014-11-05

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319168173

Language

eng

Publication classification

E1.1 Full written paper - refereed

Copyright notice

2015, Springer International Publishing Switzerland

Editor/Contributor(s)

Cremers D, Reid I, Saito H, Yang MH

Title of proceedings

ACCV 2014 : 12th Asian Conference on Computer Vision

Event

Computer Vision. Asian Conference (12th : 2014 : Singapore)

Publisher

Springer

Place of publication

Cham, Switzerland

Series

Lecture Notes in Computer Science

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