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Quantitative evaluation of face detection and tracking algorithms for head pose estimation in mobile platforms

机译:用于移动平台头部姿势估计的面部检测和跟踪算法的定量评估

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Face detection, face tracking and head pose estimation are commonly utilized in many computer vision applications related to face recognition, expression analysis, augmented reality and human computer interaction. Many different types of face detection and face tracking algorithms have been proposed by different research groups and based on the target platforms and applications, these algorithms have their own strengths and limitations. Yet a comprehensive intra and inter approach evaluation against a single data set is not available in the literature. In this paper, we present a comprehensive evaluation carried out on a set of selected face detection and tracking algorithms with respect to their accuracy, performance and robustness on both PC and mobile platforms.
机译:面部检测,面部跟踪和头部姿势估计通常用于与人面部识别,表达分析,增强现实和人机交互相关的许多计算机视觉应用。不同的研究组提出了许多不同类型的面部检测和面部跟踪算法,并基于目标平台和应用,这些算法具有自己的优点和局限性。然而,文献中不可用针对单个数据集进行全面的帧内帧间评估。在本文中,我们提出了一组选定的面部检测和跟踪算法进行了一系列综合评估,以及PC和移动平台的准确性,性能和鲁棒性。

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