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Research Article | Open Access

Face image retrieval based on shape and texture feature fusion

School of Information and Control Engineering, Nanjing University of Information Science and Technology, China.
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Abstract

Humongous amounts of data bring various challenges to face image retrieval. This paper proposes an efficient method to solve those problems. Firstly, we use accurate facial landmark locations as shape features. Secondly, we utilise shape priors to provide discriminative texture features for convolutional neural networks. These shape and texture features are fused to make the learned representation more robust. Finally, in order to increase efficiency, a coarse-to-fine search mechanism is exploited to efficiently find similar objects. Extensive experiments on the CASIA-WebFace, MSRA-CFW, and LFW datasets illustrate the superiority of our method.

Keywords

face retrievalconvolutional neural networks (CNNs)coarse-to-fine

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Computational Visual Media
Volume 3 Issue 4,
December 2017
Pages 359-368
DOI: 10.1007/s41095-017-0091-7

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Cite this article:
Lu Z, Yang J, Liu Q. Face image retrieval based on shape and texture feature fusion. Computational Visual Media, 2017, 3(4): 359-368. https://doi.org/10.1007/s41095-017-0091-7

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Revised: 27 February 2017
Accepted: 26 May 2017
Published: 02 August 2017
© The Author(s) 2017

This article is published with open access at Springerlink.com

The articles published in this journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Other papers from this open access journal are available free of charge from http://www.springer.com/journal/41095. To submit a manuscript, please go to https://www.editorialmanager.com/cvmj.