Using Graph Model for Face Analysis

Cai, Deng; He, Xiaofei; Han, Jiawei

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https://hdl.handle.net/2142/11097 Copy

Description

Title

Using Graph Model for Face Analysis

Author(s)

• Cai, Deng
• He, Xiaofei
• Han, Jiawei

Issue Date

2005-09

Keyword(s)

computer science

Abstract

Previous work has demonstrated that the image variations of many objects (human faces in particular) under variable lighting can be effectively modelled by low dimensional linear spaces. The typical methods for learning a face subspace include Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Locality Preserving Projection (LPP). Theoretical analysis shows that all these three methods can be obtained from different graph models which correspond to different geometrical structures. In this paper, we systematically analyze the relationship between these three subspace methods. We shows that LPP provides a more general framework for subspace learning and a natural solution to the small sample issue in LDA. Extensive experiments on face recognition and clustering are performed on Yale, ORL and PIE databases.

Type of Resource

text

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http://hdl.handle.net/2142/11097

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