liu.seSearch for publications in DiVA
Change search
ReferencesLink to record
Permanent link

Direct link
Facial Features Tracking using Active Appearance Models
Linköping University, Department of Electrical Engineering.
2006 (English)Independent thesis Advanced level (degree of Magister), 20 points / 30 hpStudent thesis
Abstract [en]

This thesis aims at building a system capable of automatically extracting and parameterizing the position of a face and its features in images acquired from a low-end monocular camera. Such a challenging task is justified by the importance and variety of its possible applications, ranging from face and expression recognition to animation of virtual characters using video depicting real actors. The implementation includes the construction of Active Appearance Models of the human face from training images. The existing face model Candide-3 is used as a starting point, making the translation of the tracking parameters to standard MPEG-4 Facial Animation Parameters easy.

The Inverse Compositional Algorithm is employed to adapt the models to new images, working on a subspace where the appearance is "projected out" and thus focusing only on shape.

The algorithm is tested on a generic model, aiming at tracking different people’s faces, and on a specific model, considering one person only. In the former case, the need for improvements in the robustness of the system is highlighted. By contrast, the latter case gives good results regarding both quality and speed, with real time performance being a feasible goal for future developments.

Place, publisher, year, edition, pages
Universitetsbibliotek , 2006. , 80 p.
Keyword [en]
Model-Based Coding, Face Tracking, PCA, AAM, Candide Model, MPEG-4
National Category
Computer Vision and Robotics (Autonomous Systems)
URN: urn:nbn:se:liu:diva-7658ISRN: LiTH-ISY-EX--06/3965--SEOAI: diva2:22643
2006-10-09, Systemet, B, 10:10
Available from: 2006-11-10 Created: 2006-11-10

Open Access in DiVA

fulltext(7004 kB)1834 downloads
File information
File name FULLTEXT01.pdfFile size 7004 kBChecksum SHA-1
Type fulltextMimetype application/pdf

By organisation
Department of Electrical Engineering
Computer Vision and Robotics (Autonomous Systems)

Search outside of DiVA

GoogleGoogle Scholar
Total: 1834 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 2234 hits
ReferencesLink to record
Permanent link

Direct link