NDLI: Real-Time Nonlinear Facial Feature Extraction Using Cholesky Decomposition and QR Decomposition for Face Recognition

Content Provider	IEEE Xplore Digital Library
Author	Yunhui He
Copyright Year	2009
Abstract	In this paper, we propose an efficient and effective method for extracting nonlinear discriminative facial features for real-time face recognition tasks. The optimal nonlinear discriminative features of face images are obtained by performing Cholesky decomposition and QR Decomposition only once respectively, which could be implemented using the existing fast algorithms. Since the proposed method does not solve the generalized eigenequation, the high numerical stability is achieved. Moreover, because there is no need to compute the mean of classes and the mean of total samples in the proposed method, the computational complexity is reduced greatly. Thus, the real-time performance for face recognition is guaranteed. The experiments on two standard face databases show that the proposed method can achieve better performance compared with linear facial features extraction method.
Starting Page	306
Ending Page	310
File Size	278217
Page Count	5
File Format	PDF
ISBN	9780769535593
DOI	10.1109/ICECT.2009.45
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Face recognition Scattering QR decomposition kernel method Data mining Matrix decomposition Helium Computational complexity Cholesky decomposition Information science feature extraction face recognition Linear discriminant analysis Kernel Facial features
Content Type	Text
Resource Type	Article

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