Paper Info
Title
Automatic Keypoint Detection on 3D Faces Using a Dictionary of Local Shapes
Abstract
Key points on 3D surfaces are points that can be extracted repeatably over a wide range of 3D imaging conditions. They are used in many 3D shape processing applications, for example, to establish a set of initial correspondences across a pair of surfaces to be matched. Typically, key points are extracted using extremal values of a function over the 3D surface, such as the descriptor map for Gaussian curvature. That approach works well for salient points, such as the nose-tip, but can not be used with other less pronounced local shapes. In this paper, we present an automatic method to detect key points on 3D faces, where these key points are locally similar to a set of previously learnt shapes, constituting a 'local shape dictionary'. The local shapes are learnt at a set of 14 manually-placed landmark positions on the human face. Local shapes are characterised by a set of 10 shape descriptors computed over a range of scales. For each landmark, the proportion of face meshes that have an associated key point detection is used as a performance indicator. Repeatability of the extracted key points is measured across the FRGC v2 database.
Year
DOI
Venue
2011
10.1109/3DIMPVT.2011.33
3DIMPVT
Keywords
Field
DocType
learnt shape,automatic keypoint detection,manually-placed landmark position,key point detection,human face,local shape dictionary,local shape,local shapes,shape processing application,face mesh,key point,pronounced local shape,shape,face recognition,histograms,three dimensional,feature extraction,databases,gaussian curvature
Computer vision,Object detection,Facial recognition system,Histogram,Polygon mesh,Pattern recognition,Computer science,Feature extraction,Artificial intelligence,Landmark,Salient,Gaussian curvature
Conference
Citations 
PageRank 
References 
11
0.59
19
Authors
3
Name
Order
Citations
PageRank
Clement Creusot1723.79
Nick Pears241030.57
Jim Austin3116766.82