Paper Info
Title
Optimal shape space and searching in ASM based face alignment
Abstract
The Active Shape Models (ASM) is composed of two parts: the ASM shape model and the ASM search The standard ASM, with the shape variance components all discarded and searching in image subspace and shape subspace independently, has blind searching and unstable search result In this paper, we propose a novel idea, called Optimal Shape Subspace, for optimizing ASM search It is constructed by both main shape and shape variance information It allows the reconstructed shape to vary more than that reconstructed in the standard ASM shape space, hence is more expressive in representing shapes in real life A cost function is developed, based on a careful study on the search process especially regarding relations between the ASM shape model and the ASM search An Optimal Searching method using the feedback provided by the evaluation cost can significantly improve the performance of ASM alignment This is demonstrated by experimental results.
Year
DOI
Venue
2004
10.1007/978-3-540-30548-4_12
SINOBIOMETRICS
Keywords
Field
DocType
standard asm shape space,asm shape model,main shape,standard asm,reconstructed shape,face alignment,search process,asm alignment,shape variance component,shape subspace,optimizing asm search,optimal shape space,cost function,active shape model
Shape space,Active shape model,Computer vision,Vector space,Subspace topology,Computer science,Reconstruction error,Artificial intelligence,Biometrics,Face detection,Abstract machine
Conference
Volume
ISSN
ISBN
3338
0302-9743
3-540-24029-2
Citations 
PageRank 
References 
0
0.34
7
Authors
5
Name
Order
Citations
PageRank
Lianghua He116515.94
Stan Z. Li28951535.26
Jianzhong Zhou330.74
Li Zhao422832.89
Cairong Zou541527.19