Exemplar extraction using spatio-temporal hierarchical agglomerative clustering for face recognition in video

John See, Chikkannan Eswaran

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

10 Citations (Scopus)

Abstract

Many recent works have attempted to improve object recognition by exploiting temporal dynamics, an intrinsic property of video sequences. In this paper, a new spatio-temporal hierarchical agglomerative clustering (STHAC) method is proposed for automatic extraction of face exemplars for face recognition in video sequences. Two variants of STHAC are presented -a global variety that unifies spatial and temporal distances between points, and a local variety that introduces perturbation of distances based on a local spatio-temporal neighborhood criterion. Faces that are nearest to the cluster means are chosen as exemplars for the testing stage, where subjects in the test video sequences are recognized using a probabilistic-based classifier. Extensive evaluation on a face video database demonstrates the effectiveness of our proposed method, and the significance of incorporating temporal information for exemplar extraction.

Original languageEnglish
Title of host publication2011 International Conference on Computer Vision
PublisherIEEE
Pages1481-1486
Number of pages6
ISBN (Electronic)9781457711022
ISBN (Print)9781457711015
DOIs
Publication statusPublished - 12 Jan 2012
Event2011 IEEE International Conference on Computer Vision - Barcelona, Spain
Duration: 6 Nov 201113 Nov 2011

Conference

Conference2011 IEEE International Conference on Computer Vision
Abbreviated titleICCV 2011
Country/TerritorySpain
CityBarcelona
Period6/11/1113/11/11

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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