Title | ||
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Maximizing intra-individual correlations for face recognition across pose differences |
Abstract | ||
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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 |
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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 Li | 1 | 222 | 14.22 |
Shiguang Shan | 2 | 6322 | 283.75 |
Xilin Chen | 3 | 6291 | 306.27 |
Wen Gao | 4 | 11374 | 741.77 |