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Directional Correlation Filter Bank for Robust Head Pose Estimation and Face Recognition

机译：方向相关滤波器组，用于鲁棒的头部姿势估计和面部识别

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摘要

During the past few decades, face recognition has been an active research area in pattern recognition and computer vision due to its wide range of applications. However, one of the most challenging problems encountered by face recognition is the difficulty of handling large head pose variations. Therefore, the efficient and effective head pose estimation is a critical step of face recognition. In this paper, a novel feature extraction framework, called Directional Correlation Filter Bank (DCFB), is presented for head pose estimation. Specifically, in the proposed framework, the 1-Dimensional Optimal Tradeoff Filters (1D-OTF) corresponding to different head poses are simultaneously and jointly designed in the low-dimensional linear subspace. Different from the traditional methods that heavily rely on the precise localization of the key facial feature points, our proposed framework exploits the frequency domain of the face images, which effectively captures the high-order statistics of faces. As a result, the obtained features are compact and discriminative. Experimental results on public face databases with large head pose variations show the superior performance obtained by the proposed framework on the tasks of both head pose estimation and face recognition.
机译：在过去的几十年中，由于其广泛的应用，面部识别一直是模式识别和计算机视觉领域的活跃研究领域。然而，面部识别遇到的最具挑战性的问题之一是难以处理较大的头部姿势变化。因此，有效的头部姿态估计是面部识别的关键步骤。本文提出了一种新颖的特征提取框架，称为方向相关滤波器库（DCFB），用于头部姿态估计。具体而言，在提出的框架中，在低维线性子空间中同时并联合设计了对应于不同头部姿势的一维最佳折衷滤波器（1D-OTF）。与传统的方法高度依赖于关键面部特征点的精确定位不同，我们提出的框架利用了面部图像的频域，可以有效地捕获面部的高阶统计量。结果，所获得的特征是紧凑且可区分的。在具有较大头部姿势变化的公共面部数据库上的实验结果表明，所提出的框架在头部姿势估计和面部识别这两个任务上均获得了优异的性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第13期|1923063.1-1923063.10|共10页
  • 作者

    Chen Si; Yan Dong; Yan Yan;

  • 作者单位

    Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China;

    Xiamen Univ Fujian Key Lab Sensing & Comp Smart City Sch Informat Sci & Engn Xiamen 361005 Peoples R China;

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