Paper Info
Title
3d Face Model Fitting For Recognition
Abstract
This paper presents an automatic efficient method to fit a statistical deformation model of the human face to 3D scan data. In a global to local fitting scheme, the shape parameters of this model are optimized such that the produced instance of the model accurately fits the 3D scan data of the input face. To increase the expressiveness of the model and to produce a tighter fit of the model, our method fits a set of predefined face components and blends these components afterwards. Quantitative evaluation shows an improvement of the fitting results when multiple components are used instead of one. Compared to existing methods, our fully automatic method achieves a higher accuracy of the fitting results. The accurately generated face instances are manifold meshes without noise and holes, and can be effectively used for 3D face recognition: We achieve 97.5% correct identification for 876 queries in the UND face set with 3D faces. Our results show that contour curve based face matching outperforms landmark based face matching.
Year
DOI
Venue
2008
10.1007/978-3-540-88693-8_48
COMPUTER VISION - ECCV 2008, PT IV, PROCEEDINGS
Keywords
Field
DocType
shape parameter
Face matching,Computer vision,Facial recognition system,Polygon mesh,Pattern recognition,Computer science,Artificial intelligence,Landmark,Manifold,Machine learning,Expressivity
Conference
Volume
ISSN
Citations 
5305
0302-9743
11
PageRank 
References 
Authors
0.62
15
2
Name
Order
Citations
PageRank
Frank B. ter Haar1746.24
Remco C. Veltkamp22127157.19