Deep autoencoders for attribute preserving face de-identification

Abstract

The mass availability of mobile devices equipped with cameras has lead to increased public privacy concerns in recent years. Face de-identification is a necessary first step towards anonymity preservation, and can be trivially solved by blurring or concealing detected faces. However, such naive privacy protection methods are both ineffective and unsatisfying, producing a visually unpleasant result. In this paper, we tackle face de-identification using Deep Autoencoders, by finetuning the encoder to perform face de-identification. We present various methods to finetune the encoder in both a supervised and unsupervised fashion to preserve facial attributes, while generating new faces which are both visually and quantitatively different from the original ones. Furthermore, we quantify the realism and naturalness of the resulting faces by introducing a diversity metric to measure the distinctiveness of the new faces. Experimental results show that the proposed methods can generate new faces with different person identity labels, while maintaining the facelike nature and diversity of the input face images.