Real time face detection from color video stream based on PCA method

Rajkiran Gottumukkal, V. Asari
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引用次数: 22

Abstract

We present a face detection system capable of detection of faces in real time from a streaming color video. Currently this system is able to detect faces as long as both the eyes are visible in the image plane. Extracting skin color regions from a color image is the first step in this system. Skin color detection is used to segment regions of the image that correspond to face regions based on pixel color. Under normal illumination conditions, skin color takes small regions of the color space. By using this information, we can classify each pixel of the image as skin region or non-skin region. By scanning the skin regions, regions that do not have shape of a face are removed. Principle Component Analysis (PCA) is used to classify if a particular skin region is a face or a non-face. The PCA algorithm is trained for frontal view faces only. The system is tested with images captured by a surveillance camera in real time.
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基于PCA的彩色视频流实时人脸检测
我们提出了一种能够从流媒体彩色视频中实时检测人脸的人脸检测系统。目前,该系统能够检测人脸,只要两只眼睛都在图像平面上可见。从彩色图像中提取肤色区域是该系统的第一步。皮肤颜色检测用于基于像素颜色分割图像中与人脸区域对应的区域。在正常光照条件下,肤色占据了色彩空间的一小块区域。利用这些信息,我们可以将图像的每个像素划分为皮肤区域或非皮肤区域。通过扫描皮肤区域,去除不具有面部形状的区域。主成分分析(PCA)是用来分类如果一个特定的皮肤区域是脸或非脸。PCA算法仅针对正面视图人脸进行训练。该系统通过监控摄像头实时捕获的图像进行了测试。
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