Change search
CiteExportLink to record
Permanent link

Direct link
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Tracking Head Yaw by Interpolation of Template Responses
Georgia Institute of Technology.ORCID iD: 0000-0003-4616-189X
2004 (English)In: Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’04) Volume 5 - Volume 05, Washington DC: IEEE Computer Society, 2004, Vol. 5, 83- p.Conference paper, Published paper (Refereed)
Resource type
Abstract [en]

We propose an appearance based machine learning architecturethat estimates and tracks in real time largerange head yaw given a single non-calibrated monoculargrayscale low resolution image sequence of the head. Thearchitecture is composed of five parallel template detectors,a Radial Basis Function Network and two Kalman filters.The template detectors are five view-specific images of thehead ranging across full profiles in discrete steps of 45 degrees.The Radial Basis Function Network interpolates theresponse vector from the normalized correlation of the inputimage and the 5 template detectors. The first Kalman filtermodels the position and velocity of the response vector infive dimensional space. The second is a running averagethat filters the scalar output of the network. We assume thehead image has been closely detected and segmented, that itundergoes only limited roll and pitch and that there are nosharp contrasts in illumination. The architecture is personindependentand is robust to changes in appearance, gestureand global illumination. The goals of this paper are,one, to measure the performance of the architecture, two,to asses the impact the temporal information gained fromvideo has on accuracy and stability and three, to determinethe effects of relaxing our assumptions.

Place, publisher, year, edition, pages
Washington DC: IEEE Computer Society, 2004. Vol. 5, 83- p.
Keyword [en]
Computer Vision, face recognition, face tracking, ada-boosting, machine learning, pattern recognition
National Category
Computer Science Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
URN: urn:nbn:se:kth:diva-184700OAI: diva2:916498
Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’04)

QC 20160426

Available from: 2016-04-03 Created: 2016-04-03 Last updated: 2016-04-26Bibliographically approved

Open Access in DiVA

fulltext(336 kB)