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A Surveillance System Using CNN for Face Recognition with Object, Human and Face Detection

机译:使用CNN进行人,人和人脸检测的人脸识别监控系统

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Recently, surveillance system plays an important role in solving several crimes by replacing human to watch monitors. Not only many functions are complicatedly integrated to a system but also a system is evolved to capture statistical data to extract useful information. But integrating many functions should be considered to make it have reduced processing time because a system has limited processing ability. If a system considers moving objects, it could reduce processing time because surveillance system normally has static background that is useless information. People and face detection are performed in detected objects. Detected faces are recognized using CNN(Convolutional Neural Network). The processing time of the proposed system is reduced and true rate of face recognition is 72.7% under varying distance from 2m to 5m.
机译:近来,监视系统通过代替人类来监视监视器,在解决若干犯罪中起着重要作用。不仅许多功能复杂地集成到系统中,而且系统也不断发展以捕获统计数据以提取有用的信息。但是,由于系统的处理能力有限,因此应考虑集成许多功能以减少处理时间。如果系统考虑移动物体,则可以减少处理时间,因为监视系统通常具有静态背景,即无用的信息。在检测到的对象中执行人物和面部检测。使用CNN(卷积神经网络)识别检测到的面部。在从2m到5m的不同距离下,所提出的系统的处理时间得以减少,并且人脸识别的真实率为72.7%。

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