Fingerprint Image Reconstruction from Standard Templates

Cappelli, Raffaele; Lumini, Alessandra; Maio, Dario; Maltoni, Davide

doi:10.1109/tpami.2007.1087

Cited by 270 publications

(189 citation statements)

References 16 publications

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“…But the impact of type-II atack should not be underestimated, since it can be launched against all fingerprint systems where the same finger has been enrolled. It should be noted that this performance can not be directly compared to the performance reported in [3], since (i) only type-I attack was considered in [3]; (ii) only the first impression (with large image size and good image quality) of each finger was used for reconstruction in [3]; and (iii) different fingerprint matchers have very different ability in dealing with artificial fingerprints, as shown in [3].…”

Section: Methodsmentioning

confidence: 99%

“…Cappelli et al [3] estimated orientation field by fitting an orientation field model to the orientations at minutiae. Both these methods have a minimum requirement on the number of minutiae.…”

Section: Orientation Field Reconstructionmentioning

confidence: 99%

“…In other words, minutiae extraction algorithms have been traditionally viewed as a "one-way" transform. Recently, however, this belief has been challenged by some researchers [1][2][3] who were successful in reconstructing a fingerprint image from the given minutiae template.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, streamlines that terminated due to distance constraint between adjacent streamlines generate spurious minutiae. Cappelli et al [3] estimated the orientation field by fitting a modified model proposed in [5] to the minutiae directions. Gabor filtering is iteratively performed starting from minutiae on an image initialized by local minutiae pattern.…”

Section: Introductionmentioning

confidence: 99%

“…Specifically, a new algorithm is proposed to reconstruct the continuous phase of FM model from minutiae. The advantages of our approach over existing approaches to fingerprint reconstruction [1][2][3] are: (i) the complete fingerprint can be reconstructed, and (ii) the reconstructed fingerprint contains very few spurious minutiae. The proposed reconstruction algorithm has been quantitatively assessed by matching reconstructed fingerprints against the original fingerprints (termed as type-I attack) and against different impressions of the original fingerprints (termed as type-II attack) using a commercial fingerprint SDK, Neurotechnology VeriFinger 4.2 [12].…”

Section: Introductionmentioning

confidence: 99%

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FM Model Based Fingerprint Reconstruction from Minutiae Template

Feng

Jain

2009

Advances in Biometrics

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Abstract. Minutiae-based representation is the most widely adopted fingerprint representation scheme. The compactness of minutiae template has created an impression that the minutiae template does not contain sufficient information to allow the reconstruction of the original fingerprint image. This belief has now been shown to be false; several algorithms have been proposed that can reconstruct fingerprint images from minutiae templates. However, these reconstruction techniques have a common weak point: many spurious minutiae, not included in the original minutiae template, are generated in the reconstructed image. Moreover, some of these techniques can only reconstruct a partial fingerprint. In this paper, a novel fingerprint reconstruction algorithm is proposed, which not only reconstructs the whole fingerprint, but the reconstructed fingerprint contains very few spurious minutiae. A fingerprint image is modeled as a 2D Frequency Modulation (FM) signal whose phase consists of the continuous part and the spiral part (which corresponds to minutiae). An algorithm is proposed to reconstruct the continuous phase from minutiae. The proposed reconstruction algorithm has been evaluated with respect to the success rates of type-I attack (match the reconstructed fingerprint against the original fingerprint) and type-II attack (match the reconstructed fingerprint against the different impressions of the original fingerprint) using a commercial fingerprint recognition system. Both types of attacks were shown to be successful in deceiving the fingerprint system.

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Section: Methodsmentioning

confidence: 99%

Section: Orientation Field Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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FM Model Based Fingerprint Reconstruction from Minutiae Template

Feng

Jain

2009

Advances in Biometrics

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Recent Progress of Flexible Image Sensors for Biomedical Applications

2021

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irradiating X-rays, have become indispensable approaches for diagnosing diseases. [4] Other similar applications may include imaging based on positron emission tomography using gamma rays, [10] deep ultraviolet resonant Raman imaging of a cell, [11] histopathology images using visible light, [12] and electroencephalography through nearinfrared (NIR) light [13] (Figure 1).An image sensor, or an imager, is an essential electronic device that supports such type of bioimaging technology. Image sensors have two main components: single or multiple optical sensors (for detecting light) and circuits (for reading the output from optical sensors). Multiple optical sensors are arranged in an array with specific rows and columns. By reading light signals from the sensors using readout circuits, the spatial and/or temporal distribution-localization of biological information is determined, while its dynamics can be analyzed as an image. Two important applications of such bioimaging techniques are static imaging, which involves techniques, such as X-ray and CT, and dynamic imaging, which concerns object detection such as pulse, [14] blood oxygen levels, [15,16] and blood flow. [17] In static imaging, the imaging speed is not required as only little dynamic movements exist, but it requires high resolution and high sensitivity. Meanwhile, in dynamic imaging, high resolution is not required, but high-speed imaging is essential (Figure 2).Recently, the miniaturization of semiconductor devices has led to the development of imaging devices for wearable electronics. As a typical example, a smartwatch integrates small optical sensors that can continuously monitor biological information, such as pulse, blood pressure, and blood oxygen levels, for the long-term by simply wearing the sensors in direct contact with the skin.The flexibility of image sensors is improving to reduce the discomfort caused by such wearable electronics. Because hard and heavy devices cause relatively high discomfort, their prolonged and continuous use increases the psychological and physical burden on the user. Thus, a flexible imaging device that uses soft materials and thin substrates to reduce the device thickness and weight is an effective approach for load reduction. Further, flexible devices are easier to bend and attach to the bioplane than traditional solid devices. Consequently, problems of inaccuracy or intermittency of data acquisition caused by fluctuations in the physical distance between the target skin surface and the device can be reduced. Therefore, flexible imaging Flexible image sensors have attracted increasing attention as new imaging devices owing to their lightness, softness, and bendability. Since light can measure inside information from outside of the body, optical-imaging-based approaches, such as X-rays, are widely used for disease diagnosis in hospitals. Unlike conventional sensors, flexible image sensors are soft and can be directly attached to a curved surface, such as the skin, for continuous measurement of biometric information wi...

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Attacks via record multiplicity on cancelable biometrics templates

Cai

2013

Concurrency and Computation

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Various template protection techniques have been developed in the past few years, among which cancelable biometrics is a very popular and efficient one. It uses a noninvertible transformation to map an original template to a transformed domain to prevent the recovery of the original template from a compromised transformed template. Generally, cross-template attacks over cancellable template schemes are evaluated through statistic independence metrics. In this paper, we investigate approaches to launch attacks through cryptanalysis. Four typical cancelable fingerprint template design algorithms have been investigated by applying the attack via record multiplicity attack to retrieve the original template. Concrete attack examples are also given to make the demonstration more intuitive and comprehensive. The results show that all of them are vulnerable if an attacker can obtain multiple transformed templates and their corresponding transformation parameters.

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Fingerprint Image Reconstruction from Standard Templates

Cited by 270 publications

References 16 publications

FM Model Based Fingerprint Reconstruction from Minutiae Template

FM Model Based Fingerprint Reconstruction from Minutiae Template

Recent Progress of Flexible Image Sensors for Biomedical Applications

Attacks via record multiplicity on cancelable biometrics templates

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