Illumination invariant representation for privacy preserving face identification

Most effective face recognition methods store biometric information in the clear. Doing so exposes those systems to the risk of identity theft and violation of privacy. This problem significantly narrows the practical use of face recognition technology. Recent methods for privacy preserving face recognition address face verification task. Most of them are unable to generalize to unseen conditions and require a large number of images of every user for training. We address the problem of face identification, which is more useful in security applications, and propose a binary, illumination invariant representation that can be easily integrated with various efficient cryptographic tools for protection. We propose several privacy preserving applications for our representation and test it on a number of benchmark databases to show its robustness to severe illumination changes, occlusions, and some other appearance variations.