Fingerprint liveness detection based on histograms of invariant gradients

Security of fingerprint authentication systems remains threatened by the presentation of spoof artifacts. Most current mitigation approaches rely upon the fingerprint liveness detection as the main anti-spoofing mechanisms. However, liveness detection algorithms are not robust to sensor variations. In other words, typical liveness detection algorithms need to be retrained and adapted to each and every sensor used for fingerprint capture. In this paper, inspired by popular invariant feature descriptors such as histograms of oriented gradients (HOG) and the scale invariant feature transform (SIFT), we propose a new invariant descriptor of fingerprint ridge texture called histograms of invariant gradients (HIG). The proposed descriptor is designed to preserve robustness to variations in gradient positions. Spoofed fingerprints are detected using multiple histograms of invariant gradients computed from spatial neighborhoods within the fingerprint. Results show that proposed method achieves an average accuracy comparable to the best algorithms of the Fingerprint Liveness Detection Competition 2013, while being applicable with no change to multiple acquisition sensors.