A preliminary study on identifying sensors from iris images

Kalka, Nathan D.; Bartlow, Nick; Čukić, Bojan; Ross, Arun

doi:10.1109/cvprw.2015.7301319

Cited by 17 publications

(9 citation statements)

References 13 publications

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“…They investigated the differentiability of the sensors in the CASIA-Iris V4 database by exploiting their PRNU and concluded that the equal error rates (EERs) and respective thresholds fluctuate considerably, depending on the sensor. Other work by Kalka et al [4] regarding the differentiability of iris sensor showed varying results as well, while studies conducted on fingerprint (FP) sensors by Bartlow et al [2] showed more satisfactory results.…”

Section: Introductionmentioning

confidence: 97%

“…On the one hand, it was assumed that this high variation could be caused by the correlated data that was used to generate the sensor's PRNU FP, since all images investigated in [3] have a very similar image content. On the other hand, Kalka et al [4] concluded that the variations are caused by the absence of the PRNU in saturated pixels (pixel intensity = 255) or under saturated pixels (pixel intensity = 0) for different images in the data sets. Furthermore, Uhl and Höller [3] suspected that multiple sensors may have been used for the acquisition of the CASIA-Iris V4 subsets.…”

Section: Introductionmentioning

confidence: 98%

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PRNU enhancement effects on biometric source sensor attribution

Debiasi

Uhl

2017

IET biom.

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Identifying the source camera of a digital image using the photo response non-uniformity (PRNU) is known as camera identification. Since digital image sensors are widely used in biometrics, it is natural to perform this investigation with biometric sensors. In this study, the authors focus on a slightly different task, which consists in clustering images with the same source sensor in a data set possibly containing images from multiple unknown distinct biometric sensors. Previous work showed unclear results because of the low quality of the extracted PRNU. They adopt different PRNU enhancement techniques together with the generation of PRNU fingerprints from uncorrelated data in order to clarify the results. Thus they propose extensions of existing source sensor attribution techniques which make use of uncorrelated data from known sensors and apply them in conjunction with existing clustering techniques. All techniques are evaluated on simulated data sets containing images from multiple sensors. The effects of the different PRNU enhancement approaches on the clustering outcome are measured by considering the relation between cohesion and separation of the clusters. Finally, an assessment on whether the PRNU enhancement techniques have been able to improve the results is given.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 98%

PRNU enhancement effects on biometric source sensor attribution

Debiasi

Uhl

2017

IET biom.

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“…The results from Höller et al [9], where the discriminative power of five iris sensors from the CASIA-Iris V4 database has been evaluated show high variations. Other work by Kalka et al [38] regarding the differentiability of iris sensor showed varying results, while studies conducted on fingerprint sensors by Bartlow et al [30] showed more satisfactory results. In order for PRNU fingerprints beeing useful as an authentication measure for biometric systems, the sources of the poor differentiation results have to be determined.…”

mentioning

confidence: 95%

“…In order for PRNU fingerprints beeing useful as an authentication measure for biometric systems, the sources of the poor differentiation results have to be determined. Some possible explanations are given in [38] and [9] and consist of the highly correlated data of biometric datasets, saturated pixels and the use of multiple sensors of the same model. An additional caveat for the PRNU extraction is the image content.…”

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confidence: 99%

Identifying iris sensors from iris images

Debiasi¹,

Kauba²,

Uhl³

2017

Iris and Periocular Biometric Recognition

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“…More recently, the principle of PRNU has been used to perform sensor identification in the context of iris biometrics by processing the near-infrared (NIR) ocular images acquired by typical iris sensors [43,27,17,15,16,10,11,36,8]. In this case, sensor identification (or device identification) can be used in conjunction with biometric recognition to authenticate both the identity of a device (e.g., a smartphone) as well as the individual using the device [22].…”

Section: Introductionmentioning

confidence: 99%

Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition

Banerjee

Mirjalili

Ross

2019

2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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The principle of Photo Response Non-Uniformity (PRNU) is used to link an image with its source, i.e., the sensor that produced it. In this work, we investigate if it is possible to modify an iris image acquired using one sensor in order to spoof the PRNU noise pattern of a different sensor. In this regard, we develop an image perturbation routine that iteratively modifies blocks of pixels in the original iris image such that its PRNU pattern approaches that of a target sensor. Experiments indicate the efficacy of the proposed perturbation method in spoofing PRNU patterns present in an iris image whilst still retaining its biometric content.

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A preliminary study on identifying sensors from iris images

Cited by 17 publications

References 13 publications

PRNU enhancement effects on biometric source sensor attribution

PRNU enhancement effects on biometric source sensor attribution

Identifying iris sensors from iris images

Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition

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