A Machine Learning Based Secure Face Verification Scheme and Its Applications to Digital Surveillance

• Main Authors: Huan-Chih Wang, 王煥智
• Other Authors: 吳家麟
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/h75bme

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 106 === Face Verification is a well known image analysis application and wildly used for recognizing individuals in contemporary society. However, in real applications, some of recognition systems ignore the importance of protecting the facial images that are used for verification. If the facial images are not protected, malicious people can steal and copy the images to disguise as someone else. To conquer this problem, we design a secure face verification system that can also protect the facial images to be imitated. In our work, we use the DeepID2 convolutional neural network to extract the feature of a facial image and use the EM algorithm to do the facial verification problem. In order to keep the facial images privacy, we use the homomorphic encryption scheme to encrypt the facial data and compute the EM algorithm in the ciphertext domain. Based on difference privacy concerns, we build up three face recognition systems for surveillance or entry and exist control of a local community. Lastly, we conduct experiments of accuracy and time consuming and compare pros can cons between these systems.