Optimal metric selection for improved multi-pose face recognition with group information
Access Status
Authors
Date
2012Type
Metadata
Show full item recordCitation
Source Title
Source Conference
Additional URLs
ISBN
ISSN
Remarks
© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
NOTICE: This is the author’s version of a work in which changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.
Collection
Abstract
We address the limitation of sparse representation based classification with group information for multi-pose face recognition. First, we observe that the key issue of such classification problem lies in the choice of the metric norm of the residual vectors, which represent the fitness of each class. Then we point out that limitation of the current sparse representation classification algorithms is the wrong choice of the ℓ2 norm, which does not match with data statistics as these residual values may be considerably non-Gaussian. We propose an explicit but effective solution using ℓp norm and explain theoretically and numerically why such metric norm would be able to suppress outliers and thus can significantly improve classification performance comparable to the state-of-arts algorithms on some challenging datasets.
Related items
Showing items related by title, author, creator and subject.
-
Zhang, X.; Pham, DucSon; Venkatesh, S.; Liu, Wan-Quan; Phung, D. (2015)Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation ...
-
Wang, C.; Zhang, Q.; Liu, Wan-Quan; Liu, Y.; Miao, L. (2018)The salient facial feature discovery is one of the important research tasks in ethnical group face recognition. In this paper, we first construct an ethnical group face dataset including Chinese Uyghur, Tibetan, and Korean. ...
-
Qiu, H.; Chen, Xiaoming; Liu, W.; Zhou, Guanglu; Wang, Y.; Lai, J. (2012)In this paper we apply a recently proposed Lagrange Dual Method (LDM) to design a new Sparse Representation-based Classification (LDM-SRC) algorithm for robust face recognition problem. The proposed approach improves the ...