Face image retrieval based on shape and texture feature fusion

被引：3

作者：

Lu Z. ^{[1
]}

Yang J. ^{[1
]}

Liu Q. ^{[1
]}

机构：

[1] School of Information and Control Engineering, Nanjing University of Information Science and Technology, Nanjing

来源：

Computational Visual Media | 2017年 / 3卷 / 04期

基金：

英国科研创新办公室;

关键词：

coarse-to-fine; convolutional neural networks (CNNs); face retrieval;

D O I：

10.1007/s41095-017-0091-7

中图分类号：

TN911 [通信理论];

学科分类号：

081002 ;

摘要：

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-tofine search mechanism is exploited to efficiently find similar objects. Extensive experiments on the CASIAWebFace, MSRA-CFW, and LFW datasets illustrate the superiority of our method. © 2017, The Author(s).

引用

页码：359 / 368

页数：9

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