Appearance-based smile intensity estimation by cascaded support vector machines

Keiji Shimada, Tetsu Matsukawa, Yoshihiro Noguchi, Takio Kurita

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

10 Citations (Scopus)


Facial expression recognition is one of the most challenging research area in the image recognition field and has been studied actively for a long time. Especially, we think that smile is important facial expression to communicate well between human beings and also between human and machines. Therefore, if we can detect smile and also estimate its intensity at low calculation cost and high accuracy, it will raise the possibility of inviting many new applications in the future. In this paper, we focus on smile in facial expressions and study feature extraction methods to detect a smile and estimate its intensity only by facial appearance information (Facial parts detection, not required). We use Local Intensity Histogram (LIH), Center-Symmetric Local Binary Pattern (CS-LBP) or features concatenated LIH and CS-LBP to train Support Vector Machine (SVM) for smile detection. Moreover, we construct SVM smile detector as a cascaded structure both to keep the performance and reduce the calculation cost, and estimate the smile intensity by posterior probability. As a consequence, we achieved both low calculation cost and high performance with practical images and we also implemented the proposed methods to the PC demonstration system.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2010 Workshops - ACCV 2010 International Workshops, Revised Selected Papers
Number of pages10
Publication statusPublished - 2011
Externally publishedYes
EventInternational Workshops on Computer Vision, ACCV 2010 - Queenstown, New Zealand
Duration: Nov 8 2010Nov 9 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6468 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Workshops on Computer Vision, ACCV 2010
Country/TerritoryNew Zealand

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Appearance-based smile intensity estimation by cascaded support vector machines'. Together they form a unique fingerprint.

Cite this