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首页> 外文会议>Conference on Image Extraction, Segmentation, and Recognition Oct 22-24, 2001, Wuhan, China >Real-Time Approach to Recognize Human Face in Poor Quality Video
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Real-Time Approach to Recognize Human Face in Poor Quality Video

机译：实时识别劣质视频中人脸的方法

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摘要

This paper focuses on an effective face recognition algorithm of poor quality video data and its real-time implementation. As the fundamental step of our approach, a fast face detection method based on color information is presented. Instead of performing a pixel-based color segmentation on each single face image, we incorporate color information into a face detection scheme based on spatio-temporal filtering in image sequences, which can reduce the noises in surveillance video. Our face recognition method is based on principal component analysis (PCA) that is fairly effective and fast for surveillance video in comparison with feature-based methods. For the training set, a large database of numerous face images of each subjects, digitized at the condition of three head orientations is setup. We use separate eigenspaces for different views of head orientations, so that the collection of images taken from each view of head orientations will have its own eigenspace. For real-time implementation, a automatic face detection and recognition system with TI Digital Signal Processor(DSP) TMS320C6201 is described.
机译：本文着重研究一种有效的劣质视频数据人脸识别算法及其实时实现。作为我们方法的基本步骤，提出了一种基于颜色信息的快速人脸检测方法。代替对每个单个面部图像执行基于像素的颜色分割，我们将颜色信息合并到基于图像序列中的时空滤波的面部检测方案中，这可以减少监视视频中的噪声。我们的面部识别方法基于主成分分析（PCA），与基于特征的方法相比，该方法对于监控视频非常有效且快速。对于训练集，设置了一个大型数据库，其中包含每个受试者的大量面部图像，并在三个头部方向的条件下将其数字化。我们对头部朝向的不同视图使用单独的特征空间，以便从头部朝向的每个视图获取的图像集合将具有自己的特征空间。对于实时实现，描述了具有TI数字信号处理器（DSP）TMS320C6201的自动面部检测和识别系统。

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