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
Fingerprint scanners can be spoofed by fake fingers using moldable plastic, clay, Play-Doh, wax or gelatin. Liveness detection is an anti-spoofing method which can detect physiological signs of life from fingerprints to ensure only live fingers can be captured for enrollment or authentication. Our laboratory has demonstrated that the time-varying perspiration pattern can be used as a measure to detect liveness for fingerprint systems. Unlike spoof or cadaver fingers, live fingers have a distinctive spatial perspiration phenomenon both statically and dynamically. In this paper, a new
intensity based approach is presented which quantifies the grey level differences using histogram distribution statistics and finds distinct differences between live and non-live fingerprint images. Based on these static and dynamic features, we generate the decision rules to perform liveness classification. These methods were tested on optical, capacitive DC and electro-optical scanners using a dataset of about 58 live fingerprints, 50 spoof (made from Play-Doh and Gelatin) and 25 cadaver fingerprints. The dataset was divided into three sets: training set, validation set and test set. The training set was used to generate the classification tree model while the best tree model was decided by the validation set.
Finally, the test set was used to estimate the performance. The results are compared with the former ridge signal algorithm with new extracted features. The outcome shows that the intensity based approach and ridge signal approach can extract simple features which perform with excellent classification (about 90%~100%) for some scanners using a classification tree. The proposed liveness detection methods are purely software based, efficient and easy to be implemented for commercial use. Application of these methods can provide anti-spoofing protection for fingerprint scanners.