35 research outputs found
Humans Verification by Adopting Deep Recurrent Fingerphotos Network
يمكن اعتبار صورة الإصبع واحدة من أحدث وأكثر التقنيات البيومترية إثارة للاهتمام. يعني ذلك ببساطة صورة بصمة أصبع يتم الحصول عليها عن طريق هاتف ذكي بطريقة لا تتطلب الاتصال المباشر. يقترح هذا البحث نهجًا جديدًا للتحقق من البشر استنادًا إلى صورة الإصبع الفوتوغرافية. يُطلق عليه اسم شبكة الإصبع الفوتوغرافية العميقة المتكررة. تتألف من طبقة الإدخال، وسلسلة من الطبقات الخفية، وطبقة الإخراج والتغذية العكسية الاساسية. يعتمد هذا البحث على اخذ صور فوتوغرافية لكافة الاصابع الشخصية بشكل متسلسل. و يتمتع النظام بالقدرة على التبديل بين أوزان كل إصبع فوتوغرافي فردي وتوفير التحقق. تم انشاء قاعدة بينات من عدد كبير من صور الأصابع الفوتوغرافية، وتم تنظيمها وتقسيمها واستخدامها كمجموعة بيانات مفيدة في هذا البحث. تم التوصل الى نتائج عالية في الدقة في التحقق الشخصي عن طريق استخدام الصور الفوتوغرافية للاصابع.Fingerphoto can be considered as one of recent and interesting biometrics. It basically means a fingerprint image that is acquired by a smartphone in contactless manner. This paper proposes a new Deep Recurrent Learning (DRL) approach for verifying humans based on their fingerphoto image. It is called the Deep Recurrent Fingerphotos Network (DRFN). It compromises of input layer, sequence of hidden layers, output layer and essential feedback. The proposed DRFN sequentially accepts fingerphoto images of all personal fingers. It has the capability to change between the weights of each individual fingerphoto and provide verification. A huge number of fingerphoto images have been acquired, arranged, segmented and utilized as a useful dataset in this paper. It is named the Fingerphoto Images of Ten Fingers (FITF) dataset. Average accuracy result of 99.84 % is obtained for personal verification by exploiting fingerphotos
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
Deep Learning-Based Approaches for Contactless Fingerprints Segmentation and Extraction
Fingerprints are widely recognized as one of the most unique and reliable
characteristics of human identity. Most modern fingerprint authentication
systems rely on contact-based fingerprints, which require the use of
fingerprint scanners or fingerprint sensors for capturing fingerprints during
the authentication process. Various types of fingerprint sensors, such as
optical, capacitive, and ultrasonic sensors, employ distinct techniques to
gather and analyze fingerprint data. This dependency on specific hardware or
sensors creates a barrier or challenge for the broader adoption of fingerprint
based biometric systems. This limitation hinders the widespread adoption of
fingerprint authentication in various applications and scenarios. Border
control, healthcare systems, educational institutions, financial transactions,
and airport security face challenges when fingerprint sensors are not
universally available. To mitigate the dependence on additional hardware, the
use of contactless fingerprints has emerged as an alternative. Developing
precise fingerprint segmentation methods, accurate fingerprint extraction
tools, and reliable fingerprint matchers are crucial for the successful
implementation of a robust contactless fingerprint authentication system. This
paper focuses on the development of a deep learning-based segmentation tool for
contactless fingerprint localization and segmentation. Our system leverages
deep learning techniques to achieve high segmentation accuracy and reliable
extraction of fingerprints from contactless fingerprint images. In our
evaluation, our segmentation method demonstrated an average mean absolute error
(MAE) of 30 pixels, an error in angle prediction (EAP) of 5.92 degrees, and a
labeling accuracy of 97.46%. These results demonstrate the effectiveness of our
novel contactless fingerprint segmentation and extraction tools
Deep Learning based Fingerprint Presentation Attack Detection: A Comprehensive Survey
The vulnerabilities of fingerprint authentication systems have raised
security concerns when adapting them to highly secure access-control
applications. Therefore, Fingerprint Presentation Attack Detection (FPAD)
methods are essential for ensuring reliable fingerprint authentication. Owing
to the lack of generation capacity of traditional handcrafted based approaches,
deep learning-based FPAD has become mainstream and has achieved remarkable
performance in the past decade. Existing reviews have focused more on
hand-cratfed rather than deep learning-based methods, which are outdated. To
stimulate future research, we will concentrate only on recent
deep-learning-based FPAD methods. In this paper, we first briefly introduce the
most common Presentation Attack Instruments (PAIs) and publicly available
fingerprint Presentation Attack (PA) datasets. We then describe the existing
deep-learning FPAD by categorizing them into contact, contactless, and
smartphone-based approaches. Finally, we conclude the paper by discussing the
open challenges at the current stage and emphasizing the potential future
perspective.Comment: 29 pages, submitted to ACM computing survey journa
A Universal Anti-Spoofing Approach for Contactless Fingerprint Biometric Systems
With the increasing integration of smartphones into our daily lives,
fingerphotos are becoming a potential contactless authentication method. While
it offers convenience, it is also more vulnerable to spoofing using various
presentation attack instruments (PAI). The contactless fingerprint is an
emerging biometric authentication but has not yet been heavily investigated for
anti-spoofing. While existing anti-spoofing approaches demonstrated fair
results, they have encountered challenges in terms of universality and
scalability to detect any unseen/unknown spoofed samples. To address this
issue, we propose a universal presentation attack detection method for
contactless fingerprints, despite having limited knowledge of presentation
attack samples. We generated synthetic contactless fingerprints using StyleGAN
from live finger photos and integrating them to train a semi-supervised
ResNet-18 model. A novel joint loss function, combining the Arcface and Center
loss, is introduced with a regularization to balance between the two loss
functions and minimize the variations within the live samples while enhancing
the inter-class variations between the deepfake and live samples. We also
conducted a comprehensive comparison of different regularizations' impact on
the joint loss function for presentation attack detection (PAD) and explored
the performance of a modified ResNet-18 architecture with different activation
functions (i.e., leaky ReLU and RelU) in conjunction with Arcface and center
loss. Finally, we evaluate the performance of the model using unseen types of
spoof attacks and live data. Our proposed method achieves a Bona Fide
Classification Error Rate (BPCER) of 0.12\%, an Attack Presentation
Classification Error Rate (APCER) of 0.63\%, and an Average Classification
Error Rate (ACER) of 0.37\%