Mask2LFP: Mask-constrained Adversarial Latent Fingerprint Synthesis

Walhazi, Hajer; Maalej, Ahmed; Amara, Najoua Essoukri Ben

doi:10.1109/cw49994.2020.00049

Cited by 4 publications

(3 citation statements)

References 10 publications

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“…The field of fingerprint synthesis received a regained level of attention thanks to the use of Generative Adversarial Networks (GANs) [ [35] , [36] , [37] , [38] , [39] ]. It offers opportunities to create marks and prints without the need to release personal biometric data and use them to assess biometric systems and potentially forensic training purposes.…”

Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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Section: Friction Ridge Skin and Its Individualization Processmentioning

confidence: 99%

Interpol review of fingermarks and other body impressions (2019 – 2022)

Bécue

Champod

2023

Forensic Science International: Synergy

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“…There are different scenarios to acquire contact-based fingerprints using various sensors. While the imaging techniques are advancing with sensor variations, the output of the fingerprint sensors are classified as (i) rolled full prints covering nail-to-nail area [11,12]; (ii) plain fingerprints covering flat regions [11,13]; (iii) live-scan swipe or partial fingerprints captured from portable devices [12,14]; and (iv) latent prints captured from crime scene surfaces [13,[15][16][17][18][19][20]. Each acquisition mode can have different physical finger placement with the sensor surface and therefore exhibits various challenges which call for alternatives.…”

Section: Image Acquisitionmentioning

confidence: 99%

“…Contact-based fingerprint scanning systems occupy the larger portion of the state-of-the-art fingerprint recognition in civilian applications, while the contactless domain become attractive due to the presence of portable, compact and high-resolution cameras with different image capture strategies such as multispectral, multiview and 3D-image capture. While the imaging techniques are advancing with sensor variations, the input fingerprint images are categorized as: (i) rolled full prints, by covering the nail-to-nail region of the finger (low resolution) [11,12]; (ii) plain prints, by covering flat region of the finger [11,13]; (iii) partial prints, captured from portable devices (high resolution) [12,14]; (iv) latent prints, acquired from touch surfaces (high resolution) [13,[15][16][17][18][19][20]; (v) multispectral [21]; and (vi) contactless (2D and 3D) images [3]. Figure 1 shows variations between such conventional contact-based and contactless fingerprints.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Systems: Sensors, Image Acquisition, Interoperability and Challenges

Mohamed-Abdul-Cader

Banks

Chandran

2023

Sensors

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The fingerprint is a widely adopted biometric trait in forensic and civil applications. Fingerprint biometric systems have been investigated using contact prints and latent and contactless images which range from low to high resolution. While the imaging techniques are advancing with sensor variations, the input fingerprint images also vary. A general fingerprint recognition pipeline consists of a sensor module to acquire images, followed by feature representation, matching and decision modules. In the sensor module, the image quality of the biometric traits significantly affects the biometric system’s accuracy and performance. Imaging modality, such as contact and contactless, plays a key role in poor image quality, and therefore, paying attention to imaging modality is important to obtain better performance. Further, underlying physical principles and the working of the sensor can lead to their own forms of distortions during acquisition. There are certain challenges in each module of the fingerprint recognition pipeline, particularly sensors, image acquisition and feature representation. Present reviews in fingerprint systems only analyze the imaging techniques in fingerprint sensing that have existed for a decade. However, the latest emerging trends and recent advances in fingerprint sensing, image acquisition and their challenges have been left behind. Since the present reviews are either obsolete or restricted to a particular subset of the fingerprint systems, this work comprehensively analyzes the state of the art in the field of contact-based, contactless 2D and 3D fingerprint systems and their challenges in the aspects of sensors, image acquisition and interoperability. It outlines the open issues and challenges encountered in fingerprint systems, such as fingerprint performance, environmental factors, acceptability and interoperability, and alternate directions are proposed for a better fingerprint system.

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