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首页> 外文期刊>Mathematical Problems in Engineering >Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition
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Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

机译：深受学习的姿势不变图像分析与3D面部识别中的应用

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摘要

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L-2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View AverageHalf Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support VectorMachine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.
机译：面部识别旨在基于面部特征的人建立一个人的身份，并且由于面部歧管的复杂性质，这是一个具有挑战性的问题。广泛的面部识别应用程序基于分类技术，并且将类标签分配给属于未知类的测试图像。在本文中，提出了一种姿势不变性地学习多视图3D面识别方法，并旨在解决两个问题：通过识别和验证设置面临对准和面部识别。所提出的对齐算法能够处理正面以及轮廓面部图像。它采用鼻尖启发式的姿势学习方法来估计脸部的采集姿势，然后使用L-2规范最小化粗糙到细小的鼻尖对齐。然后，通过使用从鼻尖对准的知识学习的知识通过转换对齐整个面。灵感来自左半面（LHF）和右半面（RHF）的内在面部对称，深受学习（d）多视图的平均焓面（D-MVAHF）特征用于使用深卷积神经网络进行面部识别（DCNN ）。对于面部验证，采用D-MVAHF - 支持Vectormachine（D-MVAHF-SVM）方法。通过在四个数据库中进行的广泛实验证明了所提出的方法的性能：GavaBDB，Bosphorus，UMB-DB和FRGC V2.0。结果表明，与现有最先进的方法相比，该拟议方法产生卓越的性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第13期|3547416.1-3547416.21|共21页
  • 作者

    Ratyal Naeem; Taj Imtiaz Ahmad; Sajid Muhammad; Mahmood Anzar; Razzaq Sohail; Dar Saadat Hanif; Ali Nouman; Usman Muhammad; Baig Mirza Jabbar Aziz; Mussadiq Usman;

  • 作者单位

    Mirpur Univ Sci & Technol MUST Dept Elect Engn Mirpur 10250 Ajk Pakistan|Capital Univ Sci & Technol Vis & Pattern Recognit Syst Res Grp Islamabad 45750 Pakistan;

    Capital Univ Sci & Technol Vis & Pattern Recognit Syst Res Grp Islamabad 45750 Pakistan;

    Mirpur Univ Sci & Technol MUST Dept Elect Engn Mirpur 10250 Ajk Pakistan|Capital Univ Sci & Technol Vis & Pattern Recognit Syst Res Grp Islamabad 45750 Pakistan;

    Mirpur Univ Sci & Technol MUST Dept Elect Engn Mirpur 10250 Ajk Pakistan;

    COMSATS Univ Islamabad Dept Elect Engn Abbottabad Campus Abbottabad 22060 Pakistan;

    Mirpur Univ Sci & Technol MUST Dept Software Engn Mirpur 10250 Ajk Pakistan;

    Mirpur Univ Sci & Technol MUST Dept Software Engn Mirpur 10250 Ajk Pakistan;

    Mirpur Univ Sci & Technol MUST Dept Elect Engn Mirpur 10250 Ajk Pakistan;

    Mirpur Univ Sci & Technol MUST Dept Elect Engn Mirpur 10250 Ajk Pakistan;

    Mirpur Univ Sci & Technol MUST Dept Elect Engn Mirpur 10250 Ajk Pakistan;

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