Dynamics analysis of facial expressions for person identification

Hidenori Tanaka, Hideo Saito

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

3 Citations (Scopus)

Abstract

We propose a new method for analyzing the dynamics of facial expressions to identify persons using Active Appearance Models and accurate facial feature point tracking. Several methods have been proposed to identify persons using facial images. In most methods, variations in facial expressions are one trouble factor. However, the dynamics of facial expressions are one measure of personal characteristics. In the proposed method, facial feature points are automatically extracted using Active Appearance Models in the first frame of each video. They are then tracked using the Lucas-Kanade based feature point tracking method. Next, a temporal interval is extracted from the beginning time to the ending time of facial expression changes. Finally, a feature vector is obtained. In the identification phase, an input feature vector is classified by calculating the distance between the input vector and the training vectors using dynamic programming matching. We show the effectiveness of the proposed method using smile videos from the MMI Facial Expression Database.

Original languageEnglish
Title of host publicationComputer Analysis of Images and Patterns - 13th International Conference, CAIP 2009, Proceedings
Pages107-115
Number of pages9
DOIs
Publication statusPublished - 2009 Sep 28
Event13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009 - Munster, Germany
Duration: 2009 Sep 22009 Sep 4

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5702 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other13th International Conference on Computer Analysis of Images and Patterns, CAIP 2009
CountryGermany
CityMunster
Period09/9/209/9/4

Keywords

  • AAMs
  • DP matching
  • Facial expression analysis
  • LK-based feature point tracking
  • Person identification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Tanaka, H., & Saito, H. (2009). Dynamics analysis of facial expressions for person identification. In Computer Analysis of Images and Patterns - 13th International Conference, CAIP 2009, Proceedings (pp. 107-115). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5702 LNCS). https://doi.org/10.1007/978-3-642-03767-2_13