Recent research has shown that it is possible to spoof a variety of fingerprint scanners using some simple techniques with molds made from plastic, clay, Play-Doh, silicon, or gelatin materials. To protect against spoofing, methods of liveness detection measure physiological signs of life from fingerprints, ensuring that only live fingers are captured for enrollment or authentication. We propose a new liveness detection method based on noise analysis
along the valleys in the ridge-valley structure of fingerprint images. Unlike live fingers, which have a clear ridge-valley structure, artificial fingers have a distinct noise distribution due to the material’s properties when placed on a fingerprint scanner. Statistical features are extracted in multiresolution scales using the wavelet decomposition technique. Based on these features, liveness separation (live/nonlive) is performed using classification trees and neural networks. We test this method on the data set, that contains about 58 live, 80 spoof (50 made from Play-Doh and 30 made from gelatin), and 25 cadaver subjects for 3 different scanners. We also test this method on a second data set that contains 28 live and 28 spoof (made from silicon) subjects. Results show that we can get approximately 90.9–100% classification of spoof and live fingerprints. The proposed liveness detection method is purely software-based, and application of this method can provide antispoofing protection for fingerprint scanners.