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.

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).

scholarly journals A determinant fuzzy apparoach for intrusion detection system

2018 ◽  
Vol 7 (1.9) ◽  
pp. 245
Author(s):  
S. Vimala ◽  
V. Khanna ◽  
C. Nalini
Keyword(s):  
Data Collection ◽  
Intrusion Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Great Degree ◽  
Intrusion Detection System ◽  
Detection System ◽  
Information Mining ◽  
The Past ◽  
Recognition Systems

In MANETs, versatile hubs can impart transparently to each other without the need of predefined framework. Interruption location framework is a fundamental bit of security for MANETs. It is uncommonly convincing for identifying the Intrusions and for the most part used to supplement for other security segment. That is the reason Intrusion discovery framework (IDS) is known as the second mass of assurance for any survivable framework security. The proposed fluffy based IDSs for recognition of Intrusions in MANETs are not prepared to adjust up all sort of assaults. We have examined that all proposed fluffy based IDSs are seen as to a great degree obliged segments or qualities for data collection which is specific for a particular assault. So that these IDSs are simply recognize the particular assault in MANETs. The fluffy motor may perceive blockage from channel mistake conditions, and along these lines helps the TCP blunder discovery. Examination has been made on the issues for upgrading the steady quality and precision of the decisions in MANET. This approach offers a strategy for joining remote units' estimation comes to fruition with alliance information open or priori decided at conglomerating hubs. In our investigation work, the best need was to reduce the measure of information required for getting ready and the false alarm rate. We are chiefly endeavoring to improve the execution of a present framework rather than endeavoring to supplant current Intrusion recognition systems with an information mining approach. While current mark based Intrusion identification procedures have imperatives as communicated in the past region, they do even now give basic organizations and this normal us to choose how information mining could be used as a piece of a correlative way to deal with existing measures and improves it.

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.

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.

scholarly journals An overview of touchless 2D fingerprint recognition

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jannis Priesnitz ◽  
Christian Rathgeb ◽  
Nicolas Buchmann ◽  
Christoph Busch ◽  
Marian Margraf
Keyword(s):  
High Performance ◽  
State Of The Art ◽  
Attack Detection ◽  
Fingerprint Recognition ◽  
Research Groups ◽  
Trade Offs ◽  
Reliable Detection ◽  
Recognition Systems ◽  
Open Issues ◽  
Research Resources

AbstractTouchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work.

scholarly journals Fingerprint Presentation Attack Detection using Referential Quality Metrics and Minutiae Count

2021 ◽  
Author(s):  
Akhilesh Verma ◽  
Anshadha Gupta ◽  
Mohammad Akbar ◽  
Arun Kumar Yadav ◽  
Divakar Yadav
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
State Of The Art ◽  
User Authentication ◽  
Attack Detection ◽  
Classification Error ◽  
The Public ◽  
The Past ◽  
Biometric Systems ◽  
Presentation Attack Detection

Abstract The fingerprint presentation attack is still a major challenge in biometric systems due to its increased applications worldwide. In the past, researchers used Fingerprint Presentation Attack Detection (FPAD) for user authentication, but it suffers from reliable authentication due to less focus on reducing the ‘error rate’. In this paper, we proposed an algorithm, based on referential image quality (RIQ)-metrics and minutiae count using neural network, k-NN and SVM for FPAD. We evaluate and validate the error rate reduction with different machine learning models on the public domain, such as LivDet crossmatch dataset2015 and achieved an accuracy of 88% with a neural network, 88.6% with k-NN and 88.8% using SVM. In addition, the average classification error (ACE) score is 0.1197 for ANN, 0.1138 for k-NN and 0.1117 for SVM. Thus, the results obtained show that it was achieved a reasonable accuracy with a low ACE score with respect to other state-of-the-art methods.

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