A Keypoint Descriptor for Alignment-Free Fingerprint Matching

  • Research Area:

    Information Security

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Secure fingerprint authentication via encrypted-domain processing imposes constraints on the underlying feature extraction method: Firstly, it requires fixed-length feature vectors to be amenable to computing distances or correlations. Secondly, extra information must be stored in the clear so that the fingerprints can be aligned prior to feature extraction and secure comparison. These constraints potentially restrict the flexibility, increase computational complexity, and even reduce the security of the scheme. We desire feature vectors suitable for encrypted-domain matching while being free of the above constraints. To this end, a local neighborhood is defined around certain detected minutiae points, and features are extracted based on relative locations of close minutia points, local ridge texture and local ridge orientation. The locality of the features provides robustness to rotation and translation. Feature vectors are compared using operations that can be performed using secure primitives. The process of computing the matching scores -- genuine or impostor -- implicitly yields the best alignment without needing to store unencrypted side information at the access control device. The scheme achieves an Equal Error Rate of 1.46% on a proprietary database and 7.86% on the FVC2002 public database.