Privacy-Aware Face Recognition with Lensless Multi-pinhole Camera

Yasunori Ishii, Satoshi Sato, Takayoshi Yamashita

Abstract: Face recognition and privacy protection are closely related. A high-quality facial image is required to achieve a high accuracy in face recognition; however, this undermines the privacy of the person being photographed. From the perspective of confidentiality, storing facial images as raw data is a problem. If a low-quality facial image is used, to protect user privacy, the accuracy of recognition decreases. In this paper, we propose a method for face recognition that solves these problems. We train a neural network with an unblurred image at first, and then train the neural network with a blurred image, using the features of the neural network trained with the unblurred image, as an initial value. This makes it possible to train features that are similar to the features trained with the neural network using a high-quality image. This enables us to perform face recognition without compromising user privacy. Our method consists of a neural network for face feature extraction, which extracts suitable features for face recognition from a blurred facial image, and a face recognition neural network. After pretraining both networks, we fine-tune them in an end-to-end manner. In experiments, the proposed method achieved accuracy comparable to that of conventional face recognition methods, which take as input unblurred face images from simulations and from images captured by our camera system.