Publication

Face tracking in video sequences based on multiple local features and high-light free color information

Sofia Karygianni
2009
Student project
Abstract

This thesis presents an algorithm for face tracking in video sequences. We investigate the application of affine invariant, local features for face tracking under random poses and expressions. In order to capture as much as possible of the facial variability, a combination of region detectors is used to extract the various facial points of interest. Pairwise matching of SIFT descriptors for those regions is used to identify possible similarity transformations between consecutive frames. If the matching process does not provide satisfying candidates, various translation parameters are used to determine the set of possible candidates. The similariy transformations are finally ranked according to their compatibility with the color and orientation descriptors of the previous template. The candidate with the best score is chosen as the new template. We have applied the above method in a small data set of video sequences and found it to work well under various settings and conditions.

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Related concepts (32)
Scale-invariant feature transform
The scale-invariant feature transform (SIFT) is a computer vision algorithm to detect, describe, and match local features in images, invented by David Lowe in 1999. Applications include object recognition, robotic mapping and navigation, , 3D modeling, gesture recognition, video tracking, individual identification of wildlife and match moving. SIFT keypoints of objects are first extracted from a set of reference images and stored in a database.
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Facial motion capture is the process of electronically converting the movements of a person's face into a digital database using cameras or laser scanners. This database may then be used to produce computer graphics (CG), computer animation for movies, games, or real-time avatars. Because the motion of CG characters is derived from the movements of real people, it results in a more realistic and nuanced computer character animation than if the animation were created manually.
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