Spontaneous subtle expression recognition: Imbalanced databases and solutions

Anh Cat Le Ngo*, Raphael Chung Wei Phan, John See

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationComputer Vision. ACCV 2014
Number of pages16
ISBN (Electronic)9783319168173
ISBN (Print)9783319168166
Publication statusPublished - 2015
Event12th Asian Conference on Computer Vision 2014 - Singapore, Singapore
Duration: 1 Nov 20145 Nov 2014

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


Conference12th Asian Conference on Computer Vision 2014
Abbreviated titleACCV 2014

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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