Implementation and Evaluation of a Facial Image Obscuring Method for Person Identification to Protect Personal Data
Satoru Matsumoto, Tomoki Yoshihisa, Hideyuki Shimonishi, Tomoya Kawakami, Yuuichi Teranishi · 2024
In recent years, the use of computer systems for identifying people has become increasingly popular. Clear facial images and detailed facial features are often provided to the system to improve identification accuracy, but if they are misused, privacy can be compromised. The authors are currently building a next-generation video blog system that can do the following: capture images of passersby with cameras, blur the facial images of all the people, remove the blurring for specific people who have registered their facial images for public viewing, and then upload them as video blogs in real time in a hands-free manner. By comparing the similarities between the obscured data in the user's stored data and the data captured by the camera and immediately obscured and sent to the edge server, the authors devised a system that enables person identification but does not pass clear facial images and features to the system. The authors focused on the possibility of using noise strength added to features and random seeding of features as a common quasi-encryption key to protect privacy. This paper assesses the extent to which the intensity of noise in this system obscures person identification. The results show that privacy can be protected by adding epsilon noise below a certain strength to the features, and the noise strength and the random number seed for noise generation can be used as a common quasi-cryptographic key.