Fake Finger Detection by Skin Distortion Analysis

Abstract

Abstract-Attacking fingerprint-based biometric systems by presenting fake fingers at the sensor could be a serious threat for unattended applications. This work introduces a new approach for discriminating fake fingers from real ones, based on the analysis of skin distortion. The user is required to move the finger while pressing it against the scanner surface, thus deliberately exaggerating the skin distortion. Novel techniques for extracting, encoding and comparing skin distortion information are formally defined and systematically evaluated over a test set of real and fake fingers. The proposed approach is privacy friendly and does not require additional expensive hardware besides a fingerprint scanner capable of capturing and delivering frames at proper rate. The experimental results indicate the new approach to be a very promising technique for making fingerprint recognition systems more robust against fake-finger-based spoofing attempts.

Fake Face Database and Preprocessing

“…Knowing the weights of the training images and a new test face image, a nearest neighbour approach determines the identity of the face. [9] can be viewed as a generalization of PCA [5]. While PCA de-correlates the input data using secondorder statistics and thereby generates compressed data with minimum mean-squared re-projection error, ICA minimizes both second-order and higher-order dependencies in the input.…”

Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns

“…In the hardware approach a specific device is added to the sensor in order to detect particular properties of a living trait such as the blood pressure [6], skin distortion [7], or the odor [8]. In the software approach, which is used in this work, fake traits are detected once the sample has been acquired with a standard sensor.…”

Fake Finger Detection by Finger Color Change Analysis

“…It also does not require the use of expensive hardware as in [12] and has no implication on the privacy of the user as it does not reveal the medical condition of the person. In addition, it also does not require careful interaction of the user with the scanner as in [6] and thus can be readily deployed for mass users without prior user training.…”

“…3. Analysis of dynamic properties of the finger: analyzes the properties such as pulse oximetry, blood pulsation, perspiration, skin elasticity and distortion [6]. The former two approaches can only detect a dead or an entire fake finger but cannot differentiate false layer attached to the finger.…”