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Layered Scene Reconstruction from Multiple Light Field Camera Views Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Local Fractional Order Derivative Vector Quantization Pattern for Face Recognition

Authors : Jing Li, Nong Sang, Changxin Gao

Published in: Computer Vision – ACCV 2016

Publisher: Springer International Publishing

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Abstract

Previous works have shown that fractional order derivative can give a better image description compared with conventional integral one in applications of edge detection, image segmentation, image restoration, and so on. Motivated by this conclusion, in this paper, we propose a novel local image descriptor, local fractional order derivative vector quantization pattern (fVQP), based on image local directional fractional order derivative feature vector and vector quantization method for face recognition. Compared with image integral order derivative information based local binary pattern (LBP), local derivative pattern (LDP) and local directional derivative pattern (LDDP), our fVQP image descriptor has the advantages of better image recognition performance and robust to noise. Extensive experimental results conducted on four benchmark face databases demonstrate the superior performance of our fVQP compared with existing state-of-the-art descriptors for face recognition in terms of recognition rate.

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previous chapter Illumination-Recovered Pose Normalization for Unconstrained Face Recognition
next chapter Learning Facial Point Response for Alignment by Purely Convolutional Network
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Metadata
Title
Local Fractional Order Derivative Vector Quantization Pattern for Face Recognition
Authors
Jing Li
Nong Sang
Changxin Gao
Copyright Year
2017
Book
Computer Vision – ACCV 2016

Print ISBN: 978-3-319-54186-0

Electronic ISBN: 978-3-319-54187-7

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-54187-7_16

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