Deep Fingerprint Matching from Contactless to Contact Fingerprints for Increased Interoperability

Contactless fingerprint matching is a common form of biometric security today. Most smartphones and associated apps now let users opt into using this form of biometric security. However, it’s difficult to match a finger-photo to a fingerprint because of perspective distortion occurring at the edges of the finger-photo, so direct matching using conventional methods will not be as accurate due to a lack of sufficient matching minutiae points. To address this issue, we propose a deep model, Perspective Distortion Rectification Model (PDRM), to estimate the fingerprint correspondence for finger-photo images in order to recover more minutiae points. Not only do we determine the feasibility of matching synthesized fingerprints from finger-photos, but we also show that matching a finger-photo to a fingerprint directly is possible by using our proposed Coupled Generative Adversarial Network (CpGAN) verifier. The results from our PDRM show that our method for creating synthetic fingerprints from finger-photos provides a more accurate matching (AUC=96.4%, EER= 8.9%) than just using the same commercial matcher to match finger-photo and fingerprints directly (AUC=92.1%, EER=15.7%). Finally, our proposed CpGAN verifier provides the best matching accuracy with AUC=98.4% and EER=6.3%

Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network

Fingerphoto images captured using a smartphone are successfully used to verify the individuals that have enabled several applications. This work presents a novel algorithm for fingerphoto verification using a nested residual block: Finger-NestNet. The proposed Finger-NestNet architecture is designed with three consecutive convolution blocks followed by a series of nested residual blocks to achieve reliable fingerphoto verification. This paper also presents the interpretability of the proposed method using four different visualization techniques that can shed light on the critical regions in the fingerphoto biometrics that can contribute to the reliable verification performance of the proposed method. Extensive experiments are performed on the fingerphoto dataset comprised of 196 unique fingers collected from 52 unique data subjects using an iPhone6S. Experimental results indicate the improved verification of the proposed method compared to six different existing methods with EER = 1.15%.Comment: a preprint paper accepted in wacv2023 worksho

An overview of touchless 2D fingerprint recognition

Touchless fingerprint recognition represents a rapidly growing field of research which has been studied for more than a decade. Through a touchless acquisition process, many issues of touch-based systems are circumvented, e.g., the presence of latent fingerprints or distortions caused by pressing fingers on a sensor surface. However, touchless fingerprint recognition systems reveal new challenges. In particular, a reliable detection and focusing of a presented finger as well as an appropriate preprocessing of the acquired finger image represent the most crucial tasks. Also, further issues, e.g., interoperability between touchless and touch-based fingerprints or presentation attack detection, are currently investigated by different research groups. Many works have been proposed so far to put touchless fingerprint recognition into practice. Published approaches range from self identification scenarios with commodity devices, e.g., smartphones, to high performance on-the-move deployments paving the way for new fingerprint recognition application scenarios.This work summarizes the state-of-the-art in the field of touchless 2D fingerprint recognition at each stage of the recognition process. Additionally, technical considerations and trade-offs of the presented methods are discussed along with open issues and challenges. An overview of available research resources completes the work

Double-blind comparative trial of parenteral lorazepam and papaveretum in premedication

Lorazepam, a new sedative drug of the benzodiazepine group, was compared in a double-blind study with a papaveretumjhyoscine mixture in a series of 50 patients awaiting surgery. No difference between the drugs in terms of sedation or side-effects was detected.S. Afr. Med. J., 48, 862 (1974)

Network-based Fingerprint Authentication System Using a Mobile Device

Abstract—　Fingerprint-based user authentication is highly effective in networked services such as electronic payment, but conventional authentication solutions have problems in cost, usability and security. To resolve these problems, we propose a touch-less fingerprint authentication solution, in which a mobile device\u27s built-in camera is used to capture fingerprint image, and then it is sent to the server to determine the identity of the user. We designed and implemented a prototype as an application on the Android OS, onsisting of capture, preprocessing, and matching stages. The experimental results prove that our proposed solution using a smart device is feasible and it also serves to resolve some potential problems in the touch-based fingerprint technology. Keywords—Fingerprint Authentication; E-payment; touch-less; network-based; mobile device; Androi