On the utility of canonical correlation analysis for domain adaptation in multi-view headpose estimation

K. R. Anoop, Ramanathan Subramanian, Vassilios Vonikakis, K. R. Ramakrishnan, Stefan Winkler

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

Abstract

The utility of canonical correlation analysis (CCA) for domain adaptation (DA) in the context of multi-view head pose estimation is examined in this work. We consider the three problems studied in [1], where different DA approaches are explored to transfer head pose-related knowledge from an extensively labeled source dataset to a sparsely labeled target set, whose attributes are vastly different from the source. CCA is found to benefit DA for all the three problems, and the use of a covariance profile-based diagonality score (DS) also improves classification performance with respect to a nearest neighbor (NN) classifier.

Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
EditorsJean-Luc Dugelay, Andre Morin
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4708-4712
Number of pages5
ISBN (Electronic)9781479983391
ISBN (Print)9781479983391
DOIs
Publication statusPublished - 9 Dec 2015
Externally publishedYes
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December

Conference

ConferenceIEEE International Conference on Image Processing, ICIP 2015
CountryCanada
CityQuebec City
Period27/09/1530/09/15

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