Face Recognition with Renewable and Privacy Preserving Binary Templates

Abstract

This paper considers generating binary feature vectors from biometric face data such that their privacy can be protected using recently introduced helper data systems. We explain how the binary feature vectors can be derived and investigate their statistical properties. Experimental results for a subset of the FERET and Caltech databases show that there is only a slight degradation in classification results when using the binary rather than the real-valued feature vectors. Finally, the scheme to extract the binary vectors is combined with a helper data scheme leading to renewable and privacy preserving facial templates with acceptable classification results provided that the within-class variation is not too large.