Fingerprints Image Spoof Detection and Classification (original) (raw)
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Classification of Fingerprint Images to Real vs. Spoof
Biometric identification is becoming a leading technology for identity management and security systems. Nonetheless, the use of counterfeit elastic fingerprints (“spoofing”) may break these measures. In this paper we address the problem of fingerprint spoofing based solely on image features extracted from 2D fingerprint images. By combining several lowaccuracy methods, a robust high-performance classifier for real vs. fake fingerprint images is constructed. Its high accuracy is demonstrated on a large fingerprint database. The method thus shows high potential for improving existing fingerprint authentication devices.
Fingerprint matching, spoof and liveness detection: classification and literature review
Frontiers of Computer Science, 2020
Fingerprint matching, spoof mitigation and liveness detection are the trendiest biometric techniques, mostly because of their stability through life, uniqueness and their least risk of invasion. In recent decade, several techniques are presented to address these challenges over well-known data-sets. This study provides a comprehensive review on the fingerprint algorithms and techniques which have been published in the last few decades. It divides the research on fingerprint into nine different approaches including feature based, fuzzy logic, holistic, image enhancement, latent, conventional machine learning, deep learning, template matching and miscellaneous techniques. Among these, deep learning approach has outperformed other approaches and gained significant attention for future research. By reviewing fingerprint literature, it is historically divided into four eras based on 106 referred papers and their cumulative citations.
To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.
Spoofing Protection for Biometric Systems
International Journal of Science Technology & Engineering
A biometric system is a computer system used to identify people based on their behavioral and physiological characteristics. In such kinds of systems, the security is still a question mark because of various types of intruders and attacks. This problem can be solved by improving the security using some efficient algorithms available. This paper introduces a novel software-based fake detection method that can be used in multiple biometric systems (fingerprint, iris, face, palm print) to detect different types of fraudulent access attempts through the use of image quality assessment. The proposed approach uses 25 general image quality features extracted from a single image to distinguish between real and fake samples. The system proves to be efficient for the protection against different spoofing attacks.
Fake fingerprint liveness detection based on micro and macro features
International Journal of Biometrics, 2019
Fingerprint is the most hopeful biometric authentication that can specifically identify a person from their exclusive features. In the proposed approach, a novel software-based classification method is presented to classify between fake and real fingerprint. The intention of the proposed system is to improve the security of biometric identification system. The statistical techniques are good for micro features but not well for macro. In this paper, we present a novel combination of local Haralick micro texture features with macro features derived from neighbourhood gray-tone difference matrix (NGTDM) to generate an effective feature vector. Combined extracted features of training and testing images are passed to support vector machine for discriminating live and fake fingerprints. The proposed approach is experimented and validated on ATVS dataset and LivDet2011 dataset. The proposed approach has achieved good accuracy and less error rate in comparison with previously studied techniques.
A fingerprint spoof detection based on MLP and SVM
2012
We introduce a fingerprint spoof detection technique based on MLP and SVM that combines several features. The proposed technique is evaluated on two scenarios: (i) when an impostor can perform consecutive attempts to be considered authentic; and, (ii) when the system deals with fingerprints from elderly people. In order to analyze these scenarios, a database was developed. The results show that the proposed combination of features increases the system performance in at least 33.56% and that the average error increases as more attempts for acceptance are allowed. The SVM classifier presents better performance in almost all the tested configurations. However, MLP is more accurate with biometrics from elderly people.
On the Vulnerability of Fingerprint Verification Systems to Fake Fingerprints Attacks
Proceedings 40th Annual 2006 International Carnahan Conference on Security Technology, 2006
A new method to generate gummy fingers is presented. A medium-size fake fingerprint database is described and two different fingerprint verification systems are evaluated on it. Three different scenarios are considered in the experiments, namely: enrollment and test with real fingerprints, enrollment and test with fake fingerprints, and enrollment with real fingerprints and test with fake fingerprints. Results for an optical and a thermal sweeping sensors are given. Both systems are shown to be vulnerable to direct attacks.
To ensure the actual presence of a real legitimate trait in contrast to a fake self-manufactured synthetic or reconstructed sample is a significant problem in biometric authentication, which requires the development of new and efficient protection measures. In this paper, we present a novel software-based fake detection method that can be used in multiple biometric systems to detect different types of fraudulent access attempts. The objective of the proposed system is to enhance the security of biometric recognition frameworks, by adding liveness assessment in a fast, user-friendly, and non-intrusive manner, through the use of image quality assessment. The proposed approach presents a very low degree of complexity, which makes it suitable for real-time applications, using 25 general image quality features extracted from one image (i.e., the same acquired for authentication purposes) to distinguish between legitimate and impostor samples. The experimental results, obtained on publicly available data sets of fingerprint, iris, and 2D face, show that the proposed method is highly competitive compared with other state-of-the-art approaches and that the analysis of the general image quality of real biometric samples reveals highly valuable information that may be very efficiently used to discriminate them from fake traits.