Optimisation and comparison framework for monocular camera-based face tracking

Simple item page
aut.researcherMarks, Stefan
dc.contributor.authorMarks, S
dc.contributor.authorWindsor, J
dc.contributor.authorWünsche, B
dc.date.accessioned2011-08-24T03:44:33Z
dc.date.available2011-08-24T03:44:33Z
dc.date.copyright2009
dc.date.issued2009
dc.descriptionTracking the position and orientation of the human face with respect to a camera has valuable applications in human computer interaction (HCI). Examples are navigating through a virtual environment, controlling objects using head gestures, and enabling avatars in a virtual environment to reflect the user's behaviour. Tracking performance can be heavily influenced by environmental parameters. Developers and users of face tracking plugins without computer vision experience need guidelines how to optimise face tracking performance in real world set-ups and they need measures how environmental parameters influence the results. In this paper we develop a qualitative framework for determining ideal working conditions of face tracking algorithms. We apply our framework to a commercially available face tracking solution and present the results of this analysis.
dc.description.abstractTracking the position and orientation of the human face with respect to a camera has valuable applications in human computer interaction (HCI). Examples are navigating through a virtual environment, controlling objects using head gestures, and enabling avatars in a virtual environment to reflect the user’s behaviour. Tracking performance can be heavily influenced by environmental parameters. Developers and users of face tracking plugins without computer vision experience need guidelines how to optimise face tracking performance in real world set-ups and they need measures how environmental parameters influence the results. In this paper we develop a qualitative framework for determining ideal working conditions of face tracking algorithms. We apply our framework to a commercially available face tracking solution and present the results of this analysis.
dc.identifier.citationConference proceedings from 2009 24th International Conference Image and Vision Computing New Zealand (IVCNZ09), pp.243 - 248
dc.identifier.doi10.1109/IVCNZ.2009.5378402
dc.identifier.isbn978-1-4244-4697-1
dc.identifier.issn2151-2205
dc.identifier.urihttps://hdl.handle.net/10292/1868
dc.publisherIEEE
dc.relation.urihttp://dx.doi.org/10.1109/IVCNZ.2009.5378402
dc.rightsCopyright © 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.accessrightsOpenAccess
dc.subjectApplication software
dc.subjectAvatars
dc.subjectCameras
dc.subjectComputer vision
dc.subjectEmployee welfare
dc.subjectFace detection
dc.subjectGuidelines
dc.subjectHuman computer interaction
dc.subjectNavigation
dc.subjectVirtual environment
dc.titleOptimisation and comparison framework for monocular camera-based face tracking
dc.typeJournal Article
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
marks2009optimisationtracking (2).pdf
Size:
5.83 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
licence.htm
Size:
29.98 KB
Format:
Unknown data format
Description:
Download
Collections
School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau