Facial image de-identification using identiy subspace decomposition

How to conceal the identity of a human face without covering the facial image? This is the question investigated in this work. Leveraging the high dimensional feature representation of a human face in an Active Appearance Model (AAM), a novel method called the identity subspace decomposition (ISD) method is proposed. Using ISD, the AAM feature space is deposed into an identity sensitive subspace and an identity insensitive subspace. By replacing the feature values in the identity sensitive subspace with the averaged values of k individuals, one may realize a k-anonymity de-identification process on facial images. We developed a heuristic approach to empirically select the AAM features corresponding to the identity sensitive subspace. We showed that after applying k-anonymity de-identification to AAM features in the identity sensitive subspace, the resulting facial images can no longer be distinguished by either human eyes or facial recognition algorithms.