Identifying sensors from fingerprint images

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

doi:10.1109/cvprw.2009.5204312

Cited by 30 publications

(14 citation statements)

References 6 publications

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“…Though this decrease in accuracy is minor but considering that both the cameras are from the same vendor and this variation is caused due to technological advancements (i.e., LG 4000 is an advanced version of LG 2200 camera), this is still significant. Figure 9 shows that on pre-processing the images using the proposed algorithm (with feature set II), 4 Figure 7. ROC curves illustrating the performance of the proposed algorithm using feature set I for cross-camera matching using COTS1 and COTS2 matchers on the IIITD MSI database.…”

Section: Iris Recognition With Camera Interoperabilitymentioning

confidence: 99%

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On iris camera interoperability

Arora

Vatsa

Singh

et al. 2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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Section: Iris Recognition With Camera Interoperabilitymentioning

confidence: 99%

“…Ross and Nadgir [13] used a thin-plate spine model to address fingerprint sensor interoperability. Bartlow et al [4], on the other hand, generated noise residual reference patterns for different fingerprint sensors. These patterns were used for identifying the sensor using which the test image was captured.…”

Section: Introductionmentioning

confidence: 99%

On iris camera interoperability

Arora

Vatsa

Singh

et al. 2012

2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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“…Here is the place to mention the only work on identifying biometric scanners [Bartlow et al 2009] that we are aware of because it uses exactly the same algorithm as the one proposed in [Lukas et al 2006] for identifying digital cameras. Barlow et al applied the algorithm of [Lukas et al 2006] to 16 optical and 4 capacitive fingerprint scanners.…”

Section: Digital Forensicsmentioning

confidence: 99%

Authentication of fingerprint scanners

Ivanov

Baras

2011

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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To counter certain security threats in biometric authentication systems, particularly in portable devices (e.g., phones and laptops), we have developed a technology for automated authentication of fingerprint scanners of exactly the same type, manufacturer, and model. The technology uses unique, persistent, and unalterable characteristics of the fingerprint scanners to detect attacks on the scanners, such as detecting an image containing the fingerprint pattern of the legitimate user and acquired with the authentic fingerprint scanner replaced by another image that still contains the fingerprint pattern of the legitimate user but has been acquired with another, unauthentic fingerprint scanner. The technology uses the conventional authentication steps of enrolment and verification, each of which can be implemented in a portable device, a desktop, or a remote server. The technology is extremely accurate, computationally efficient, robust in a wide range of conditions, does not require any hardware modifications, and can be added (as a software add-on) to systems already manufactured and placed into service. We have also implemented the technology in a demonstration prototype for both area and swipe scanners. Many thanks go Mrs. Kimberly Edwards who was a dependable collaborator throughtout all my study and work and on whom I relied for my most critical and iii urgent needs, be it with conferences, travel arrangements, or purchasing various equipment for my lab. This has saved me precious time when I needed it most and I greatly appreciate it. Perfectly organized, responsive, and patient, she always made time for me, giving me advice and helping me even on short notice and over weekends.

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“…The admissibility of such evidence is contingent on its integrity, i.e., it should not have been tampered with. The chain of custody [6] of the digital evidence can be established via (i) hardware identification (the device used to acquire the biometric sample) [7], and (ii) analysis at the image level. IPT construction involves imagelevel analysis and can be leveraged to determine the authenticity between a pair of biometric samples i.e., the original versus the tampered.…”

Section: Introductionmentioning

confidence: 99%

Face Phylogeny Tree Using Basis Functions

Banerjee

Ross

2020

IEEE Trans. Biom. Behav. Identity Sci.

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Photometric transformations, such as brightness and contrast adjustment, can be applied to a face image repeatedly creating a set of near-duplicate images. Identifying the original image from a set of such near-duplicates and deducing the relationship between them are important in the context of digital image forensics. This is commonly done by generating an image phylogeny treea hierarchical structure depicting the relationship between a set of near-duplicate images. In this work, we utilize three different families of basis functions to model pairwise relationships between near-duplicate images. The basis functions used in this work are orthogonal polynomials, wavelet basis functions and radial basis functions. We perform extensive experiments to assess the performance of the proposed method across three different modalities, namely, face, fingerprint and iris images; across different image phylogeny tree configurations; and across different types of photometric transformations. We also utilize the same basis functions to model geometric transformations and deep-learning based transformations. We also perform extensive analysis of each basis function with respect to its ability to model arbitrary transformations and to distinguish between the original and the transformed images. Finally, we utilize the concept of approximate von Neumann graph entropy to explain the success and failure cases of the proposed IPT generation algorithm. Experiments indicate that the proposed algorithm generalizes well across different scenarios thereby suggesting the merits of using basis functions to model the relationship between photometrically and geometrically modified images. !• Both the authors are with the

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Identifying sensors from fingerprint images

Cited by 30 publications

References 6 publications

On iris camera interoperability

On iris camera interoperability

Authentication of fingerprint scanners

Face Phylogeny Tree Using Basis Functions

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