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Improving Fingerprint Data with Optical Coherence Tomography

Despite its extensive history, fingerprint biometrics still faces various unsolved challenges in authenticating individuals. To address these challenges, a study published in Sensors proposed a method involving a digital signal processing chain and optical coherence tomography to accurately and rapidly identify fingerprint data.

Study: Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data. Image Credit: LookerStudio/Shutterstock.com

Limitations and Challenges of Current Fingerprint Technology

In recent years, biometric technologies have become increasingly prevalent in verifying individuals. In contrast to traditional approaches, which rely on passwords or key cards, biometrics analyzes the physiological characteristics of people.

Fingerprint recognition is one of the most well-known and popular biometric methods. However, various obstacles prevent its widespread use in practical applications. Current fingerprint scanners can have trouble collecting fingerprints in various non-ideal yet crucial scenarios.

For instance, skin injury and extremely wet or dry skin can significantly deteriorate the quality of fingerprint data. In addition, infants’ skin is very soft, and their fingerprint patterns are squished when rubbed against a sensor. This restricts fingerprinting in applications like combating child trafficking and identifying babies in hospitals.

Current fingerprint systems are vulnerable to presentation attacks, where an impostor incorporates fingerprint artifacts to impersonate the target victim. The identification of such attacks is referred to as presentation attack detection (PAD), and it remains an ongoing challenge for numerous attack scenarios.

PAD-enabled fingerprint security systems need various additional special-purpose sensors for decision-making. Therefore, a simple yet effective method is needed to safely and accurately authenticate biometric data.

Optical Coherence Tomography for Investigating Fingertip Skin Anatomy

The outermost layer of human skin is divided into the dermis and epidermis. The interface between these layers consists of double-rowed connective tissue. In biometrics, this area is known as the dermal or inner fingerprint, serving as a template for the outer fingerprint.

From this area, newly produced skin cells travel to the skin’s surface, where they continually regenerate the outer fingerprint. This allows the outside fingerprint region to heal from superficial wounds that do not affect the inner fingerprint area.

The inner fingerprint region was studied as early as 1958, and no technology was available to collect the fingerprint data without damaging it. The introduction of optical coherence tomography (OCT) made it possible to examine the inner fingerprint in vivo at an appropriate resolution.

Optical coherence tomography is a noninvasive imaging technology frequently referred to as the optical counterpart of ultrasonic pulse-echo imaging. Based on optical delay, it employs low-coherence interferometry to obtain depth-resolved images from optical scattering media, such as biological tissues.

While the normal effective imaging depth of OCT is less than a millimeter, its spatial resolution can reach approximately 10 micrometers.

Using OCT and Digital Signal Processing Chain for Robust Identification and Segmentation of Fingerprint Data

In this study, researchers proposed a digital signal processing chain (DSP) for segmenting multiple fingerprints from a single OCT fingertip scan. One fingerprint was collected from the epidermis, and the other was taken from deep within the skin, at the junction between the underlying dermis and the epidermis.

The resulting 3D fingerprints were then transformed into a normal 2D grayscale representation, from which minute points were retrieved using available methodological approaches.

Optical coherence tomography data was processed in less than a second using fast GPGPU computation. Over 130 unique fingerprints were collected using OCT and compared to their traditional 2D counterparts to verify the results.

Significant Findings of the Study

The proposed method offers an inherent PAD solution without requiring additional special sensors since the fingerprint collection process is expanded into 3D with an optical coherence tomography scanner.

The quality of the segmented optical coherence tomography fingerprint data was high enough for point extraction and rapid authentication and was also compatible with standard 2D fingerprints.

While the exterior fingerprint can be significantly damaged, the inner fingerprint demonstrated strong resistance to superficial skin injury. This gives optical coherence tomography a clear edge over traditional acquisition techniques.

The proposed method has been demonstrated to function with two distinct time-domain optical coherence tomography scanners and is independent of hardware.

Future Outlooks of the Study

In future studies, researchers intend to improve the 3D-to-2D mapping to simulate the conventional touch-based capture for which current fingerprint-matching devices are built.

In addition, they plan to create a PAD mechanism that is inherently secure by utilizing the inner fingerprint region and additional skin characteristics, such as sweat ducts that are absent from current artifact fingers.

Reference

Kirfel, A., Scheer, T., Jung, N., & Busch, C. (2022). Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data. Sensors, 22(21), 8229 https://www.mdpi.com/1424-8220/22/21/8229/htm

Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.

Owais Ali

Written by

Owais Ali

NEBOSH certified Mechanical Engineer with 3 years of experience as a technical writer and editor. Owais is interested in occupational health and safety, computer hardware, industrial and mobile robotics. During his academic career, Owais worked on several research projects regarding mobile robots, notably the Autonomous Fire Fighting Mobile Robot. The designed mobile robot could navigate, detect and extinguish fire autonomously. Arduino Uno was used as the microcontroller to control the flame sensors' input and output of the flame extinguisher. Apart from his professional life, Owais is an avid book reader and a huge computer technology enthusiast and likes to keep himself updated regarding developments in the computer industry.

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