NDLI: An optimal set of code words and correntropy for rotated least squares regression

Content Provider	IEEE Xplore Digital Library
Author	Jie Gui Zhenan Sun Guangqi Hou Tieniu Tan
Copyright Year	2014
Description	Author affiliation: Nat. Lab. of Pattern Recognition, Inst. of Autom., Beijing, China (Jie Gui; Zhenan Sun; Guangqi Hou; Tieniu Tan)
Abstract	This paper presents a robust feature extraction method for face recognition based on least squares regression (LSR). Our focus is to enhance the robustness and discriminability of the LSR. First, an optimal set of code words is introduced in LSR. Compared to the traditional set of code words, this new set uses less number of code words. Furthermore, it can make the distance of the regression targets of different classes as large as possible. Then, correntropy is integrated into the LSR model for better robustness. Furthermore, considering the commonly used distance metrics such as Euclidean distance and Cosine distance in the subspace are invariant to rotation transformation, rotation is introduced as additional freedom to promote flexibility without sacrificing accuracy. Our objective function is optimized using half-quadratic (HQ) optimization, which facilitates algorithm development and convergence study. Experimental results show that our method outperforms several subspace methods for face recognition, which indicates the validity of the proposed method.
Sponsorship	IEEE
Starting Page	1
Ending Page	6
File Size	227249
Page Count	6
File Format	PDF
ISBN	9781479935840
DOI	10.1109/BTAS.2014.6996222
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-09-29
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Image databases Face recognition Robustness Face Principal component analysis
Content Type	Text
Resource Type	Article

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