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Face Classification Using Cascade Facial Detection and Convolutional Neural Network
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 Title & Authors
Face Classification Using Cascade Facial Detection and Convolutional Neural Network
Yu, Je-Hun; Sim, Kwee-Bo;
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Nowadays, there are many research for recognizing face of people using the machine vision. the machine vision is classification and analysis technology using machine that has sight such as human eyes. In this paper, we propose algorithm for classifying human face using this machine vision system. This algorithm consist of Convolutional Neural Network and cascade face detector. And using this algorithm, we classified the face of subjects. For training the face classification algorithm, 2,000, 3,000, and 4,000 images of each subject are used. Training iteration of Convolutional Neural Network had 10 and 20. Then we classified the images. In this paper, about 6,000 images was classified for effectiveness. And we implement the system that can classify the face of subjects in realtime using USB camera.
Machine vision;Convolutional Neural Network;Cascade face detector;Human-robot interaction;
 Cited by
SFTA와 AdaBoost 기반 한우의 육질 등급 분석,조현학;김은경;장은석;김광백;김성신;

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