Similarity features for facial event analysis

Peng Yang, Qingshan Liu, Dimitris Metaxas

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

3 Scopus citations


Each facial event will give rise to complex facial appearance variation. In this paper, we propose similarity features to describe the facial appearance for video-based facial event analysis. Inspired by the kernel features, for each sample, we compare it with the reference set with a similarity function, and we take the log-weighted summarization of the similarities as its similarity feature. Due to the distinctness of the apex images of facial events, we use their cluster-centers as the references. In order to capture the temporal dynamics, we use the K-means algorithm to divide the similarity features into several clusters in temporal domain, and each cluster is modeled by a Gaussian distribution. Based on the Gaussian models, we further map the similarity features into dynamic binary patterns to handle the issue of time resolution, which embed the time-warping operation implicitly. The haar-like descriptor is used to extract the visual features of facial appearance, and Adaboost is performed to learn the final classifiers. Extensive experiments carried on the Cohn-Kanade database show the promising performance of the proposed method.

Original languageEnglish (US)
Title of host publicationComputer Vision - ECCV 2008 - 10th European Conference on Computer Vision, Proceedings
PublisherSpringer Verlag
Number of pages12
EditionPART 1
ISBN (Print)3540886818, 9783540886815
StatePublished - 2008
Event10th European Conference on Computer Vision, ECCV 2008 - Marseille, France
Duration: Oct 12 2008Oct 18 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume5302 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other10th European Conference on Computer Vision, ECCV 2008

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Similarity features for facial event analysis'. Together they form a unique fingerprint.

Cite this