Fingerprint Liveness Detection Using Multiple Static Features and Random Forests

2014 ◽  
Vol 14 (04) ◽  
pp. 1450021
Author(s):  
Yanyan Guo ◽  
Xiangdong Fei ◽  
Qijun Zhao
Keyword(s):  
Fingerprint Recognition ◽  
Classification Error ◽  
Fingerprint Image ◽  
Classification Error Rate ◽  
First Order ◽  
Liveness Detection ◽  
Cepstral Analysis ◽  
Detection Approach ◽  
Fingerprint Liveness Detection ◽  
Recognition Systems

It has been demonstrated that fingerprint recognition systems are susceptible to spoofing by presenting a well-duplicated synthetic such as a gummy finger. This paper proposes a novel software-based liveness detection approach using multiple static features. Given a fingerprint image, the static features, including fingerprint coarseness, first-order statistics and intensity-based features, are extracted. Unlike previous methods, the fingerprint coarseness is modeled as multiplicative noise rather than additive noise and is extracted by cepstral analysis. A random forest classifier is employed to select effective features among the extracted features and to differentiate fake from live fingerprints. The proposed method has been evaluated on the standard database provided in the Fingerprint Liveness Detection Competition 2009 (LivDet2009). Compared with other state-of-the-art methods, the proposed method reduces the average classification error rate by more than 20%.

scholarly journals Uniform Local Binary Pattern for Fingerprint Liveness Detection in the Gaussian Pyramid

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Yujia Jiang ◽  
Xin Liu
Keyword(s):  
Local Binary Pattern ◽  
Fingerprint Recognition ◽  
Svm Classifier ◽  
Liveness Detection ◽  
Gaussian Pyramid ◽  
Detection Approach ◽  
Fingerprint Liveness Detection ◽  
Recognition Systems ◽  
Uniform Local Binary Pattern ◽  
High Recognition Accuracy

Fingerprint recognition schemas are widely used in our daily life, such as Door Security, Identification, and Phone Verification. However, the existing problem is that fingerprint recognition systems are easily tricked by fake fingerprints for collaboration. Therefore, designing a fingerprint liveness detection module in fingerprint recognition systems is necessary. To solve the above problem and discriminate true fingerprint from fake ones, a novel software-based liveness detection approach using uniform local binary pattern (ULBP) in spatial pyramid is applied to recognize fingerprint liveness in this paper. Firstly, preprocessing operation for each fingerprint is necessary. Then, to solve image rotation and scale invariance, three-layer spatial pyramids of fingerprints are introduced in this paper. Next, texture information for three layers spatial pyramids is described by using uniform local binary pattern to extract features of given fingerprints. The accuracy of our proposed method has been compared with several state-of-the-art methods in fingerprint liveness detection. Experiments based on standard databases, taken from Liveness Detection Competition 2013 composed of four different fingerprint sensors, have been carried out. Finally, classifier model based on extracted features is trained using SVM classifier. Experimental results present that our proposed method can achieve high recognition accuracy compared with other methods.

Fingerprint Presentation Attack Detection Using Transfer Learning Approach

2021 ◽  
Vol 17 (1) ◽  
pp. 53-67
Author(s):  
Rajneesh Rani ◽  
Harpreet Singh
Keyword(s):  
Transfer Learning ◽  
Attack Detection ◽  
Classification Error ◽  
Learning Approach ◽  
Biometric Authentication ◽  
The Past ◽  
Liveness Detection ◽  
Fingerprint Liveness Detection ◽  
Software And Hardware ◽  
Presentation Attack Detection

In this busy world, biometric authentication methods are serving as fast authentication means. But with growing dependencies on these systems, attackers have tried to exploit these systems through various attacks; thus, there is a strong need to protect authentication systems. Many software and hardware methods have been proposed in the past to make existing authentication systems more robust. Liveness detection/presentation attack detection is one such method that provides protection against malicious agents by detecting fake samples of biometric traits. This paper has worked on fingerprint liveness detection/presentation attack detection using transfer learning for which the authors have used a pre-trained NASNetMobile model. The experiments are performed on publicly available liveness datasets LivDet 2011 and LivDet 2013 and have obtained good results as compared to state of art techniques in terms of ACE(average classification error).

Application of Fingerprint-Matching Algorithm in Smart Gun Using Touch-Less Fingerprint Recognition System

2020 ◽  
pp. 122-128
Author(s):  
Saifullah Khalid
Keyword(s):  
Recognition System ◽  
Fingerprint Recognition ◽  
Fingerprint Matching ◽  
Fingerprint Image ◽  
Matching Algorithm ◽  
Latent Fingerprint ◽  
Recognition Systems

Fingerprint recognition systems are widely used in the field of biometrics. Many existing fingerprint sensors acquire fingerprint images as the user's fingerprint is contacted on a solid flat sensor. Because of this contact, input images from the same finger can be quite different and there are latent fingerprint issues that can lead to forgery and hygienic problems. For these reasons, a touchless fingerprint recognition system has been investigated, in which a fingerprint image can be captured without contact. While this system can solve the problems which arise through contact of the user's finger, other challenges emerge.

scholarly journals The Impact of Pressure on the Fingerprint Impression: Presentation Attack Detection Scheme

2021 ◽  
Vol 11 (17) ◽  
pp. 7883
Author(s):  
Anas Husseis ◽  
Judith Liu-Jimenez ◽  
Raul Sanchez-Reillo
Keyword(s):  
Error Rate ◽  
Attack Detection ◽  
Fingerprint Recognition ◽  
Classification Error ◽  
Final Decision ◽  
Classification Error Rate ◽  
Detection Scheme ◽  
Bona Fide ◽  
The Impact ◽  
Presentation Attack Detection

Fingerprint recognition systems have been widely deployed in authentication and verification applications, ranging from personal smartphones to border control systems. Recently, the biometric society has raised concerns about presentation attacks that aim to manipulate the biometric system’s final decision by presenting artificial fingerprint traits to the sensor. In this paper, we propose a presentation attack detection scheme that exploits the natural fingerprint phenomena, and analyzes the dynamic variation of a fingerprint’s impression when the user applies additional pressure during the presentation. For that purpose, we collected a novel dynamic dataset with an instructed acquisition scenario. Two sensing technologies are used in the data collection, thermal and optical. Additionally, we collected attack presentations using seven presentation attack instrument species considering the same acquisition circumstances. The proposed mechanism is evaluated following the directives of the standard ISO/IEC 30107. The comparison between ordinary and pressure presentations shows higher accuracy and generalizability for the latter. The proposed approach demonstrates efficient capability of detecting presentation attacks with low bona fide presentation classification error rate (BPCER) where BPCER is 0% for an optical sensor and 1.66% for a thermal sensor at 5% attack presentation classification error rate (APCER) for both.

Fingerprint Liveness Detection Based on Fake Finger Characteristics

2012 ◽  
Vol 4 (3) ◽  
pp. 1-19 ◽  
Author(s):  
Gian Luca Marcialis ◽  
Pietro Coli ◽  
Fabio Roli
Keyword(s):  
Optical Sensors ◽  
State Of The Art ◽  
Fingerprint Image ◽  
Identity Verification ◽  
Data Set ◽  
Liveness Detection ◽  
Artificial Materials ◽  
Shape Pattern ◽  
Fingerprint Liveness Detection ◽  
Verification Systems

The vitality detection of fingerprints is currently acknowledged as a serious issue for personal identity verification systems. This problem, raised some years ago, is related to the fact that the 3d shape pattern of a fingerprint can be reproduced using artificial materials. An image quite similar to that of true, alive, fingerprint, is derived if such “fake fingers” are submitted to an electronic scanner. Since introducing hardware dedicated to liveness detection in scanners is expensive, software-based solutions, based on image processing algorithms, have been proposed as alternative. So far, proposed approaches are based on features exploiting characteristics of a live finger (e.g., finger perspiration). Such features can be named live-based, or vitality-based features. In this paper, the authors propose and motivate the use of a novel kind of features exploiting characteristics noticed in the reproduction of fake fingers, that they named fake-based features. Then the authors propose a possibile implementation of this kind of features based on the power spectrum of the fingerprint image. The proposal is compared and integrated with several live-based features at the state-of-the-art, and shows very good liveness detection performances. Experiments are carried out on a data set much larger than commonly adopted ones, containing images from three different optical sensors.

scholarly journals A Novel 2D Feature Extraction Method for Fingerprints Using Minutiae Points and Their Intersections

2017 ◽  
Vol 7 (5) ◽  
pp. 2547
Author(s):  
Nibras Ar Rakib ◽  
SM Zamshed Farhan ◽  
Md Mashrur Bari Sobhan ◽  
Jia Uddin ◽  
Arafat Habib
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Fingerprint Recognition ◽  
Support Vector ◽  
Svm Classifier ◽  
Fingerprint Image ◽  
Feature Extraction Method ◽  
Proposed Model ◽  
Recognition Systems ◽  
To Come

The field of biometrics has evolved tremendously for over the last century. Yet scientists are still continuing to come up with precise and efficient algorithms to facilitate automatic fingerprint recognition systems. Like other applications, an efficient feature extraction method plays an important role in fingerprint based recognition systems. This paper proposes a novel feature extraction method using minutiae points of a fingerprint image and their intersections. In this method, initially, it calculates the ridge ends and ridge bifurcations of each fingerprint image. And then, it estimates the minutiae points for the intersection of each ridge end and ridge bifurcation. In the experimental evaluation, we tested the extracted features of our proposed model using a support vector machine (SVM) classifier and experimental results show that the proposed method can accurately classify different fingerprint images.

A review on presentation attack detection system for fake fingerprint

2020 ◽  
Vol 34 (05) ◽  
pp. 2030001 ◽  
Author(s):  
Rohit Agarwal ◽  
A. S. Jalal ◽  
K. V. Arya
Keyword(s):  
Detection System ◽  
Attack Detection ◽  
Fingerprint Recognition ◽  
The Past ◽  
Liveness Detection ◽  
Comprehensive Survey ◽  
Open Issue ◽  
Recognition Systems ◽  
Effectiveness And Efficiency ◽  
Presentation Attack Detection

Fingerprint recognition systems are susceptible to artificial spoof fingerprint attacks, like molds manufactured from polymer, gelatin or Play-Doh. Presentation attack is an open issue for fingerprint recognition systems. In a presentation attack, synthetic fingerprint which is reproduced from a real user is submitted for authentication. Different sensors are used to capture the live and fake fingerprint images. A liveness detection system has been designed to defeat different classes of spoof attacks by differentiating the features of live and fake fingerprint images. In the past few years, many hardware- and software-based approaches are suggested by researchers. However, the issues still remain challenging in terms of robustness, effectiveness and efficiency. In this paper, we explore all kinds of software-based solution to differentiate between real and fake fingerprints and present a comprehensive survey of efforts in the past to address this problem.

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