NDLI: Efficient feature selection for linear discriminant analysis and its application to face recognition

Content Provider	IEEE Xplore Digital Library
Author	Zhen Lei Shengcai Liao Li, S.Z.
Copyright Year	2012
Description	Author affiliation: Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 95 Zhongguancun Donglu, Beijing 100190, China (Zhen Lei; Shengcai Liao; Li, S.Z.)
Abstract	Feature selection is an important issue in pattern recognition. In face recognition, one of the state-of-the-art methods is that some feature selection methods (e.g., AdaBoost) are first utilized to select the most discriminative features and then the subspace learning methods (e.g., LDA) are further applied to learn the discriminant subspace for classification. However, in these methods, the objective of feature selection and subspace learning is not so consistent and the combination is not the optimal. In this paper, we propose a novel and efficient feature selection method that is designed for linear discriminant analysis (LDA). We use the Fisher criterion to select the most discriminative and appropriate features so that the objectives of feature selection and classifier learning are consistent (both follow the Fisher criterion) and the face recognition performance is expected to be improved. Experiments on FRGC v2.0 face database validate the efficacy of the proposed method.
Starting Page	1136
Ending Page	1139
File Size	136717
Page Count	4
File Format	PDF
ISBN	9781467322164
ISSN	10514651
e-ISBN	9784990644109
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-11-11
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	ICPR Org Committee
Subject Keyword	Face recognition Face Databases Redundancy Training Correlation Feature extraction
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition

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