Title
Dynamically adaptive tracking of gestures and facial expressions
Abstract
We present a dynamic data-driven framework for tracking gestures and facial expressions from monocular sequences. Our system uses two cameras, one for the face and one for the body view for processing in different scales. Specifically, and for the gesture tracking module, we track the hands and the head, obtaining as output the blobs (ellipses) of the ROIs, and we detect the shoulder positions with straight lines. For the facial expressions, we first extract the 2D facial features, using a fusion between KLT tracker and a modified Active Shape Model, and then we obtain the 3D face mask with fitting a generic model to the extracted 2D features. The main advantages of our system are (i) the adaptivity, i.e., it is robust to external conditions, e.g., lighting, and independent from the examined individual, and (ii) its computational efficiency, providing us results off- and online with a rates higher than 20fps.
Year
DOI
Venue
2006
10.1007/11758532_73
International Conference on Computational Science (3)
Keywords
Field
DocType
facial expression,active shape model
Computer vision,Active shape model,Computer science,Gesture,Active appearance model,Image segmentation,Facial expression,Artificial intelligence,Monocular,Ellipse,Facial motion capture
Conference
Volume
ISSN
ISBN
3993
0302-9743
3-540-34383-0
Citations 
PageRank 
References 
4
0.43
12
Authors
5
Name
Order
Citations
PageRank
Dimitris N. Metaxas18834952.25
Gabriel Tsechpenakis216014.47
Zhiguo Li334220.97
Yuichi Huang440.43
Atul Kanaujia569537.76