Title
Maximizing intra-individual correlations for face recognition across pose differences
Abstract
The variations of pose lead to significant performance decline in face recognition systems, which is a bottleneck in face recognition. A key problem is how to measure the similarity between two image vectors of unequal length that viewed from different pose. In this paper, we propose a novel approach for pose robust face recognition, in which the similarity is measured by correlations in a media subspace between different poses on patch level. The media subspace is constructed by canonical correlation analysis, such that the intra-individual correlations are maximized. Based on the media subspace two recognition approaches are developed. In the first, we transform non-frontal face into frontal for recognition. And in the second, we perform recognition in the media subspace with probabilistic modeling. The experimental results on FERET database demonstrate the efficiency of our approach.
Year
DOI
Venue
2009
10.1109/CVPR.2009.5206659
CVPR
Keywords
Field
DocType
image vector similarity,pose difference,face recognition,media subspace,pose estimation,probabilistic modeling,maximizing intraindividual correlation,nonfrontal face,canonical correlation analysis,correlation methods,length measurement,databases,solid modeling,probabilistic model,shape,face,ellipsoids,robustness,correlation,information processing,geometry,media
Computer vision,Facial recognition system,3D single-object recognition,Pattern recognition,Three-dimensional face recognition,Subspace topology,Canonical correlation,Computer science,Pose,Artificial intelligence,Probabilistic logic,FERET database
Conference
Volume
Issue
ISSN
2009
1
1063-6919
ISBN
Citations 
PageRank 
978-1-4244-3992-8
54
1.85
References 
Authors
16
4
Name
Order
Citations
PageRank
Annan Li122214.22
Shiguang Shan26322283.75
Xilin Chen36291306.27
Wen Gao411374741.77