Face De-Identification with Perfect Privacy Protection

Abstract

The rising concern for privacy protection and the associated legal and social responsibilities have led to extensive research into the field of face de-identification over the last decade. To date, the most successful algorithms developed for face de-identification are those based on the k-Same de-identification, which guarantee a recognition rate lower than 1/k. However, the current k-Same solutions such as k-Same-Eigen and k-Same-M all rely on a decent value of k to deliver a good privacy protection. This paper proposes a departure from a fundamental aspect shared by the current k-Same solutions and thereby introduces a new member to the family which achieves perfect privacy protection for any original face regardless of the value of k.