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Performance Analysis of Face Recognition by Face Image resolutions using CNN without Backpropergation and LDA  

Moon, Hae-Min (조선대학교 제어계측로봇공학과)
Park, Jin-Won (홍익대학교 게임학부)
Pan, Sung Bum (조선대학교 전자공학과)
Publication Information
Smart Media Journal / v.5, no.1, 2016 , pp. 24-29 More about this Journal
Abstract
To satisfy the needs of high-level intelligent surveillance system, it shall be able to extract objects and classify to identify precise information on the object. The representative method to identify one's identity is face recognition that is caused a change in the recognition rate according to environmental factors such as illumination, background and angle of camera. In this paper, we analyze the robust face recognition of face image by changing the distance through a variety of experiments. The experiment was conducted by real face images of 1m to 5m. The method of face recognition based on Linear Discriminant Analysis show the best performance in average 75.4% when a large number of face images per one person is used for training. However, face recognition based on Convolution Neural Network show the best performance in average 69.8% when the number of face images per one person is less than five. In addition, rate of low resolution face recognition decrease rapidly when the size of the face image is smaller than $15{\times}15$.
Keywords
Low resolution face image; Long distance face recognition; Linear Discriminant Analysis; Convolution Neural Network;
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