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2017 | OriginalPaper | Buchkapitel

Cost Sensitive Matrix Factorization for Face Recognition

verfasst von : Jianwu Wan, Ming Yang, Hongyuan Wang

Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2017

Verlag: Springer International Publishing

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Abstract

In this paper, we propose a cost sensitive matrix factorization (CSMF) for face recognition. To make the face representation cost sensitive, CSMF adopts a more flexible feature embedding strategy. It contains two main steps: (1) matrix factorization for the learning of latent semantic representation and (2) cost sensitive latent semantic regression. In this way, the face images are embedded into their label space with the misclassification loss minimized. The experimental results on Extended Yale B and ORL demonstrate its effectiveness.

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Titel: Cost Sensitive Matrix Factorization for Face Recognition
verfasst von: Jianwu Wan
Ming Yang
Hongyuan Wang
Verlag: Springer International Publishing
Buch: Intelligent Data Engineering and Automated Learning – IDEAL 2017
Print ISBN: 978-3-319-68934-0

Electronic ISBN: 978-3-319-68935-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68935-7_16

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