研究深度cnn模型在面部图像亲属关系验证中的应用

A. Chergui, Salim Ouchtati, S. Mavromatis, Salah Eddine Bekhouche, J. Sequeira
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引用次数: 4

摘要

基于人脸图像的亲属关系验证因其潜在的应用前景而成为一个活跃的研究课题。在本文中,我们提出了一种以两张图像作为输入,然后给出亲属关系结果(亲属关系/非亲属关系)作为输出的方法。我们的方法基于深度学习模型(ResNet)进行特征提取步骤,以及我们提出的配对特征表示函数和RankFeatures (Ttest)进行特征选择以减少特征数量,最后我们使用SVM分类器进行亲属关系验证的决策。该方法包括三个步骤:(1)人脸预处理;(2)深度特征提取和特征对表示;(3)分类。实验在五个公共数据库上进行。实验结果表明,该方法与现有方法具有可比性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Investigating Deep CNNs Models Applied in Kinship Verification through Facial Images
The kinship verification through facial images is ana ctive research topic due to its potential applications. In this paper, we propose an approach which takes two images as input then give kinship result (kinship / No-kinship) as an output. our approach based on the deep learning model (ResNet) for the feature extraction step, alongside with our proposed pair feature representation function and RankFeatures (Ttest) for feature selection to reduce the number of features finally we use the SVM classifier for the decision of kinship verification. The approach contains three steps which are: (1) face preprocessing, (2) deep features extraction and pair features representation (3) Classification. Experiments are conducted on five public databases. The experimental results show that our approach is comparable with existed approaches.
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