Proceedings ArticleDOI
Wavelet energy signature and GLCM features-based fingerprint anti-spoofing
Shankar Bhausaheb Nikam,Suneeta Agarwal +1 more
- Vol. 2, pp 717-723
TLDR
A texture-based method to spoof-proof a fingerprint biometric system using textural measures based on wavelet energy signatures and gray level co-occurrence matrix (GLCM) features to characterize fingerprint texture is proposed.Abstract:
This paper proposes a texture-based method to spoof-proof a fingerprint biometric system. The fundamental basis of this anti-spoofing method is that, real fingerprint exhibits different textural characteristics from a spoof one. Textural measures based on wavelet energy signatures and gray level co-occurrence matrix (GLCM) features are used to characterize fingerprint texture. Dimensionalities of the feature sets are reduced by running Pudilpsilas sequential forward floating selection (SFFS) algorithm. We test two feature sets independently on various classifiers like: AdaBoost.M1, support vector machine and OneR. Then, we fuse all the mentioned classifiers using the ldquoproduct rulerdquo to form a hybrid classifier. Classification rates achieved for wavelet energy signatures range from ~94.35% to ~96.71%. Likewise, classification rates for GLCM features range from ~94.82% to ~97.65%. Thus, the performance of a proposed method is very promising and it can be efficiently used to spoof-proof a real-time fingerprint biometric system.read more
Citations
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Journal ArticleDOI
Presentation attack detection methods for fingerprint recognition systems: a survey
Ctirad Sousedik,Christoph Busch +1 more
TL;DR: This study is a survey of presentation attack detection methods for fingerprints, both in terms of liveness detection and alteration detection.
Proceedings ArticleDOI
Fingerprint Liveness Detection using Binarized Statistical Image Features
TL;DR: A novel fingerprint liveness descriptor named “BSIF” is described, which, similarly to Local Binary Pattern and Local Phase Quantization-based representations, encodes the local fingerprint texture on a feature vector.
Journal ArticleDOI
Open Set Fingerprint Spoof Detection Across Novel Fabrication Materials
TL;DR: Experiments conducted on new partitions of the LivDet 2011 database designed for open set evaluation suggest a 97% increase in the error rate of the existing spoof detectors when tested using new spoof materials and up to 44% improvement in spoof detection performance across spoof materials when the proposed adaptive approach is used.
Proceedings ArticleDOI
An ensemble of one-class SVMs for fingerprint spoof detection across different fabrication materials
Yaohui Ding,Arun Ross +1 more
TL;DR: Experimental results on the LivDet2011 database show the advantages of the proposed ensemble of OC-SVMs for detecting spoofs generated from previously “unseen” materials.
Journal ArticleDOI
Evaluation of Fingerprint Liveness Detection by Machine Learning Approach - A Systematic View
Edriss Eisa Babikir Adam,Sathesh +1 more
TL;DR: This article focuses on the implementation and evaluation of suitable machine learning algorithms to detect fingerprint liveness, and includes the comparative study between Ridge-let Transform (RT) and the Machine Learning (ML) approach.
References
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Journal ArticleDOI
Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Journal ArticleDOI
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Journal ArticleDOI
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TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
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Journal ArticleDOI
Enhancing security and privacy in biometrics-based authentication systems
TL;DR: The inherent strengths of biometrics-based authentication are outlined, the weak links in systems employing biometric authentication are identified, and new solutions for eliminating these weak links are presented.