A novel facial expression database construction method based on web images

Xibo Wang, Xiaoyi Feng, Jinye Peng

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Facial expression recognition plays an important role in web images understanding. Since current databases for expression learning are usually achieved in experimental environment which are different from actual emotions, and the samples in each database is small, it is hard to train good expression classifiers with them, and which leads to a low expression classification ability, especially when recognizing expressions in web images. In this paper, a novel facial expression database construction method is suggested: First, a large-scale social label images are obtained by the Google web search with the keywords of happiness, sadness, surprise, anger, disgust and fear respectively; Then, unrelated images are filtered as junk images interactively by the hyperbolic visualization technique and the expression database is constructed. All the images in the database are from real social network, so the database is more proper to train classifiers for recognizing expressions in web images.

Original languageEnglish
Title of host publicationICIMCS 2011 - 3rd International Conference on Internet Multimedia Computing and Service, Proceedings
Pages124-127
Number of pages4
DOIs
StatePublished - 2011
Event3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011 - Chengdu, China
Duration: 5 Aug 20117 Aug 2011

Publication series

NameACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Internet Multimedia Computing and Service, ICIMCS 2011
Country/TerritoryChina
CityChengdu
Period5/08/117/08/11

Keywords

  • facial expression database
  • hyperbolic visualization
  • interactive filtering

Fingerprint

Dive into the research topics of 'A novel facial expression database construction method based on web images'. Together they form a unique fingerprint.

Cite this