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首页> 外文期刊>Multimedia Tools and Applications >A novel facial image recognition method based on perceptual hash using quintet triple binary pattern
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A novel facial image recognition method based on perceptual hash using quintet triple binary pattern

机译：一种新的基于感知散列的小型面部图像识别方法，使用Quintet三重二进制模式

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摘要

Image classification (categorization) can be considered as one of the most breathtaking domains of contemporary research. Indeed, people cannot hide their faces and related lineaments since it is highly needed for daily communications. Therefore, face recognition is extensively used in biometric applications for security and personnel attendance control. In this study, a novel face recognition method based on perceptual hash is presented. The proposed perceptual hash is utilized for preprocessing and feature extraction phases. Discrete Wavelet Transform (DWT) and a novel graph based binary pattern, called quintet triple binary pattern (QTBP), are used. Meanwhile, the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms are employed for classification task. The proposed face recognition method is tested on five well-known face datasets: AT&T, Face94, CIE, AR and LFW. Our proposed method achieved 100.0% classification accuracy for the AT&T, Face94 and CIE datasets, 99.4% for AR dataset and 97.1% classification accuracy for the LFW dataset. The time cost of the proposed method is 0(nlogn). The obtained results and comparisons distinctly indicate that our proposed has a very good classification capability with short execution time.
机译：图像分类（分类）可被视为当代研究中最令人叹为观止的域之一。实际上，人们无法隐藏他们的面孔和相关谱系，因为日常通信是强烈的。因此，面部识别广泛用于安全和人员考勤控制的生物识别应用中。在本研究中，提出了一种基于感知散列的新型面部识别方法。所提出的感知哈希用于预处理和特征提取阶段。使用离散小波变换（DWT）和基于新的基于曲线图，称为Quintet三重二进制图案（QTBP）。同时，用于分类任务的K最近邻居（KNN）和支持向量机（SVM）算法。所提出的面部识别方法在五个众所周知的脸部数据集上进行测试：AT＆T，Face94，CIE，AR和LFW。我们所提出的方法为AT＆T，Face94和CIE数据集进行了100.0％的分类准确性，AR数据集的99.4％和LFW数据集的97.1％的分类准确性。所提出方法的时间成本为0（NLogn）。所获得的结果和比较明显地表明，我们提出的具有非常好的分类能力，执行时间短。

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  • 来源
    《Multimedia Tools and Applications》 |2020年第40期|29573-29593|共21页
  • 作者

    Turker Tuncer; Sengul Dogan; Moloud Abdar; Paweł Pławiak;

  • 作者单位

    Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey;

    Department of Digital Forensics Engineering Technology Faculty Firat University Elazig Turkey;

    Institute for Intelligent Systems Research and Innovation (nSRI) Deakin University Geelong Australia;

    Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 St. F-3 31-155 Krakow Poland Polish Academy of Sciences Institute of Theoretical and Applied mformatics Baftycka 5 44-100 Gliwice Poland;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Face recognition; Quintet triple binary pattern; Perceptual hash; Machine learning; Biometrics;

    机译：人脸识别;Quintet三重二进制模式;感知哈希;机器学习;生物识别学;

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