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首页> 外文会议>2011 IEEE International Conference on Systems, Man, and Cybernetics >Real-time scale-invariant face detection on range images
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Real-time scale-invariant face detection on range images

机译：距离图像的实时比例不变面部检测

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摘要

We present a scale-invariant face detection approach based on boosted cascade classifiers using range images as input. The detector was developed to be employed as a preliminary stage for any real-time 3D face recognition system. The required computation time for this task was considerably reduced by eliminating the need for scanning an input image in multiple scales. Our experiments were performed using two well-known databases, and the proposed approach was favorably compared against a state-of-the-art face detection approach. We achieved a detection rate of 99.9% with only 0.2% of the images presenting false detections. We also evaluated the detector performance in face images presenting large pose variations and obtained detection rates as high as when using frontal face images.
机译：我们提出了一种基于不变的人脸检测方法，该方法基于使用距离图像作为输入的增强级联分类器。该检测器被开发用作任何实时3D人脸识别系统的初级阶段。通过消除对多个比例的输入图像进行扫描的需求，大大减少了此任务所需的计算时间。我们的实验是使用两个著名的数据库进行的，与最先进的人脸检测方法相比，该方法具有优势。我们仅将0.2％的图像显示为虚假检测，从而实现了99.9％的检测率。我们还评估了存在较大姿态变化的面部图像中的检测器性能，并获得了与使用正面面部图像时一样高的检测率。

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