A structure, artificial fingers have a distinct noise

A new method by merging ridge signal and valley noise analysis is analyzed for anti-spoofing in fingerprint sensorscite{c38}.This method focuses on the perspiration patterns along ridges and noise patterns along the valleys. The signals representing gray level patterns along ridges and valleys are investigated in spatial, frequency and wavelet domains. Based on these features, liveness detection is performed using standard pattern classification tools such as neural networks and classification trees. A new liveness detection method based on noise analysis along the valleys in the ridge-valley structure of fingerprint images is given. Unlike live fingers which hold a clear ridge-valley structure, artificial fingers have a distinct noise distribution due to the material properties when placed on a fingerprint scanner. Statistical features are extracted in multi resolution scales using wavelet decomposition technique. Based on these features, liveness separation is performed using classification trees and neural networks. subsubsection{Skin Elasticity based Techniques}

paragraph{Skin elasticity}

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Distortions due to the rotation and pressure of the finger on a sensor result in different elastic characteristics of the materials. Liveness can be detected by comparing these distortions through static features. The elastic deformation due to the contact of the fingertip with a plane surface was analyzed in cite{c26} since a fake fingerprint presents different deformations than a hot one. The elastic behavior of a live and a fake finger was analyzed by using a mathematical model relying on the extraction of a specific and ordered set of minutiae points. Fake fingerprint detection using skin distortion is discussed. The user moves the finger on the scanner surface, to produce skin distortion. The distortion produced by the real finger is different from that produced by fake fingers. Since fake fingers are more rigid than the skin, they produce less distortion.

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