Privacy and security of features extracted from minutiae aggregates

This paper describes our recent analysis on the security and privacy of biometric feature vectors obtained from fingerprint minutiae. A large number of contiguous regions (cuboids) are selected at random in the minutiae space, and several new features are extracted from the minutiae inside each such cuboid. Specifically, the features are extracted from the average minutia coordinate within a cuboid, the standard deviation of the minutiae coordinates, and the aggregate wall distance, i.e., the sum of distance of each minutia from the boundary of the cuboids. In terms of matching performance on a public database, the feature vectors provide an equal error rate of 3% even if the imposter is allowed to use the same local patches as the genuine user. Performance within a secure biometrics framework is evaluated by applying an LDPC code to the feature vectors and storing only the syndrome at the access control device, for use in authentication. The paper concludes with a discussion on met...

• Type: Conference
• Year: 2010
• Where: ICASSP
• Authors: Abhishek Nagar, Shantanu Rane, Anthony Vetro
• Keywords: Biometric Feature Vectors, Feature Vectors, ICASSP 2010, Minutiae, Signal Processing