Local contrast phase descriptor for fingerprint liveness detection

• We propose a new local descriptor for fingerprint liveness detection.
• It is based on the joint use of contrast and phase information.
• Image analysis is carried out in both the spatial and the transform domains.
• We generate a bi-dimensional contrast–phase histogram, used as feature vector.
• A properly trained linear-kernel SVM classifier makes the final live/fake decision.

We propose a new local descriptor for fingerprint liveness detection. The input image is analyzed both in the spatial and in the frequency domain, in order to extract information on the local amplitude contrast, and on the local behavior of the image, synthesized by considering the phase of some selected transform coefficients. These two pieces of information are used to generate a bi-dimensional contrast-phase histogram, used as feature vector associated with the image. After an appropriate feature selection, a trained linear-kernel SVM classifier makes the final live/fake decision. Experiments on the publicly available LivDet 2011 database, comprising datasets collected from various sensors, prove the proposed method to outperform the state-of-the-art liveness detection techniques.