CNN-based identity recognition system

• Main Authors: CHUN-LIN CHEN, 陳俊霖
• Other Authors: Chih-Fong Tsai
• Format: Others
• Language: zh-TW
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/drtnp8

碩士 === 國立中央大學 === 資訊管理學系在職專班 === 107 === This paper proposes a set of "CNN-based identity recognition system" for identity recognition using a computer vision library OpenCV and deep learning technology and webcam. It is expected to be applied to access control and regional security. Monitoring, advertising, or other related systems that need to be enhanced by confirming their identity. This thesis is based on Python and TensorFlow's built-in GoogLeNet CNN model. Supervised learning is used to obtain facial image features and classified by identity. This paper uses self-organizing face image data and compares GoogLeNet. The identification rate of the three versions of the model, in the neural network architecture with the highest recognition rate in the experiment, can increase the recognition rate by adding the residual network experiment. Using the neural network model of the above-mentioned best recognition rate, the OpenCV is used to load the movie to instantly recognize the character in the film to verify the practicability of the neural network model of the research training. In the verification part of the results, the paper has self-organized 14 public figures, and each public figure has at least 130 face images as training and test and verification samples, among which the best recognition rate of the neural network is in 1260 images. The recognition rate of the training sample is 100%, and the image recognition rate of the 450 images is 99.11%. The time of the instant image recognition from the face image in the film to the completion identity is about 0.1 second.