SODA-boosting and its application to gender recognition

N. Xu, Thomas S Huang

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

Abstract

In this paper we propose a novel boosting based classification algorithm, SODA-Boosting (where SODA stands for Second Order Discriminant Analysis). Unlike the conventional AdaBoost based algorithms widely applied in computer vision, SODA-Boosting does not involve time consuming procedures to search a huge feature pool in every iteration during the training stage. Instead, in each iteration SODA-Boosting efficiently computes discriminative weak classifiers in closed-form, based on reasonable hypotheses on the distribution of the weighted training samples. As an application, SODA-Boosting is employed for image based gender recognition. Experimental results on publicly available FERET database are reported. The proposed algorithm achieved accuracy comparable to state-of-the-art approaches, and demonstrated superior performance to relevant boosting based algorithms.

Original languageEnglish (US)
Title of host publicationAnalysis and Modeling of Faces and Gestures - Third International Workshop, AMFG 2007, Proceedings
Pages193-204
Number of pages12
StatePublished - Dec 1 2007
Event3rd International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2007 - Rio de Janeiro, Brazil
Duration: Oct 20 2007Oct 20 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4778 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd International Workshop on Analysis and Modeling of Faces and Gestures, AMFG 2007
CountryBrazil
CityRio de Janeiro
Period10/20/0710/20/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Xu, N., & Huang, T. S. (2007). SODA-boosting and its application to gender recognition. In Analysis and Modeling of Faces and Gestures - Third International Workshop, AMFG 2007, Proceedings (pp. 193-204). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4778 LNCS).