An adaptation framework for head-pose classification in dynamic multi-view scenarios

Anoop K. Rajagopal, Ramanathan Subramanian, Radu L. Vieriu, Elisa Ricci, Oswald Lanz, Kalpathi Ramakrishnan, Nicu Sebe

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

12 Citations (Scopus)


Multi-view head-pose estimation in low-resolution, dynamic scenes is difficult due to blurred facial appearance and perspective changes as targets move around freely in the environment. Under these conditions, acquiring sufficient training examples to learn the dynamic relationship between position, face appearance and head-pose can be very expensive. Instead, a transfer learning approach is proposed in this work. Upon learning a weighted-distance function from many examples where the target position is fixed, we adapt these weights to the scenario where target positions are varying. The adaptation framework incorporates reliability of the different face regions for pose estimation under positional variation, by transforming the target appearance to a canonical appearance corresponding to a reference scene location. Experimental results confirm effectiveness of the proposed approach, which outperforms state-of-the-art by 9.5% under relevant conditions. To aid further research on this topic, we also make DPOSE- a dynamic, multi-view head-pose dataset with ground-truth publicly available with this paper.

Original languageEnglish
Title of host publicationComputer Vision, ACCV 2012 - 11th Asian Conference on Computer Vision, Revised Selected Papers
EditorsKyoung Mu Lee, Yasuyuki Matsushita, James M. Rehg, Zhanyi Hu
Place of PublicationNetherlands
Number of pages15
EditionPART 2
ISBN (Print)9783642374432
Publication statusPublished - 2013
Externally publishedYes
Event11th Asian Conference on Computer Vision, ACCV 2012 - Daejeon, Korea, Republic of
Duration: 5 Nov 20129 Nov 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7725 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference11th Asian Conference on Computer Vision, ACCV 2012
Country/TerritoryKorea, Republic of


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