Paper Info
Title
Marginal Fisher Regression Classification for Face Recognition.
Abstract
This paper presents a novel marginal Fisher regression classification (MFRC) method by incorporating the ideas of marginal Fisher analysis (MFA) and linear regression classification (LRC). The MFRC aims at minimizing the within-class compactness over the between-class separability to find an optimal embedding matrix for the LRC so that the LRC on that subspace achieves a high discrimination for classification. Specifically, the within-class compactness is measured with the sum of distances between each sample and its neighbors within the same class with the LRC, and the between-class separability is characterized as the sum of distances between margin points and their neighboring points from different classes with the LRC. Therefore, the MFRC embodies the ideas of the LRC, Fisher analysis and manifold learning. Experiments on the FERET, PIE and AR datasets demonstrate the effectiveness of the MFRC.
Year
DOI
Venue
2015
10.1007/978-3-319-24075-6_44
ADVANCES IN MULTIMEDIA INFORMATION PROCESSING - PCM 2015, PT I
Keywords
Field
DocType
Face recognition,Nearest subspace classification,Linear regression classification,Manifold learning
Facial recognition system,Embedding,Pattern recognition,Regression,Subspace topology,Computer science,Compact space,Artificial intelligence,Nonlinear dimensionality reduction,Fisher kernel,Linear regression
Conference
Volume
ISSN
Citations 
9314
0302-9743
0
PageRank 
References 
Authors
0.34
10
5
Name
Order
Citations
PageRank
Zhong Ji116923.08
Yunlong Yu2191.29
Yanwei Pang3179891.55
Yingming Li45714.82
Zhongfei (Mark) Zhang52451164.30