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![Automatic learning of 3D fingerprint features via deep representation [86]. Automatic learning of 3D fingerprint features via deep representation [86].](profile/Am-Mahmud-Chowdhury/publication/363385196/figure/fig2/AS:11431281083616401@1662685150569/Automatic-learning-of-3D-fingerprint-features-via-deep-representation-86-Automatic.png)
Automatic learning of 3D fingerprint features via deep representation [86]. Automatic learning of 3D fingerprint features via deep representation [86].
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Contactless fingerprint identification systems have been introduced to address the deficiencies of contact-based fingerprint systems. A number of studies have been reported regarding contactless fingerprint processing, including classical image processing, the machine-learning pipeline, and a number of deep-learning-based algorithms. The deep-learn...
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Citations
... The enduring and unchanging nature of fingerprints throughout any individual's lifetime emphasize pivotal role in ensuring precision and dependability in the matters of recognition and security. The enduring biological characteristic of fingerprint continues to be essential tool in society, safeguarding individuals and communities [17][18][19][20] . Fingerprints possess valuable features including assemble, ridge crossing, bifurcation, core, enclosure, ridge ending and delta shape. ...
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.
... This technique eliminated minutiae that had short ridges, minutiae that were located in noisy regions, and minutiae that were located in ridge breaks by making use of information regarding the orientation of the ridges. In their publication [30,34,35] stage in order to make the process of minutiae filtering that came after it easier. This was done in order to improve the image quality. ...
... C. Image-based or Pattern-based Learning Options Matching pattern-based algorithms compare the fundamental fingerprint patterns of a candidate fingerprint with those of a previously stored template. These fundamental fingerprint patterns include local orientation and frequency, ridge shape, and [35], whereas Hamamoto explains a fingerprint matching method that is based on a Gabor filter. Both methods are used to compare and match fingerprint images [16]. ...
The distinctive patterns observed on human fingertips are a result of the combination of ridges and troughs present on the skin surface. These patterns achieve their complete development during the period of pregnancy and remain stable throughout an individual's lifespan. Fingerprints refer to the impressions formed by the unique patterns present on the fingertips. Bruising resulting from accidental injuries, such as cuts and burns, can temporarily compromise the visibility of fingerprints. However, after the lesion has fully healed, the original patterns will be restored. Fingerprint recognition is a highly dynamic and extensively researched domain within the realm of biometrics. The word refers to the abbreviated form of the computational technique employed to ascertain the concordance between two distinct human fingerprints. In this naturally tough pattern recognition issue, it is imperative to minimize two error rates: the False Accept Rate (FAR) and the False Reject Rate (FRR). These mistake rates are in competition with each other. The development of computing capabilities has facilitated the creation of Automated Fingerprint Authentication Systems (AFIS), leading to a substantial increase in research activity, particularly in the past twenty years. This article aims to provide a comprehensive review of the issue surrounding fingerprint recognition, including an analysis of its underlying design and implementation difficulties, along with an assessment of its potential future developments.
... In the past years, contactless fingerprint recognition has been introduced as a more convenient alternative to contactbased schemes [1], [2], [3]. In contrast to contact-based capturing schemes where the finger is pressed onto a planar surface, contactless recognition workflows do not require any contact between the subject and the capturing subsystem. ...
... In 2021, a virtual Workshop on Fingerprint Image Quality (NFIQ 2.1) was organized by the European Association for Biometrics (EAB) in cooperation with NIST and other institutions. 2 Throughout this workshop, the importance of a reliable quality assessment for fingerprint images was emphasized. Moreover, the speakers and panelists formulated the interest of extending the scope of NFIQ 2 to other capturing technologies like contactless fingerprints. ...
... This method evaluates whether a rejection of low quality samples results in a reduced False-Non-Match error Rate (FNMR). Each mated comparison is associated with a similarity score s ii and two quality scores q (1) i and q (2) i . In order to aggregate the pair of quality scores from a pair of samples to be compared, the min function is chosen as combination function: ...
We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we re-trained the NIST Fingerprint Image Quality (NFIQ) 2 method, which was originally designed for contact-based fingerprints, with a synthetic contactless fingerprint database. We evaluate the predictive performance of the resulting MCLFIQ model in terms of Error-vs.-Discard Characteristic (EDC) curves on three real-world contactless fingerprint databases using three recognition algorithms. In experiments, the MCLFIQ method is compared against the original NFIQ 2 method, a sharpness-based quality assessment algorithm developed for contactless fingerprint images and the general purpose image quality assessment method BRISQUE. Furthermore, benchmarks on four contact-based fingerprint datasets are also conducted. Obtained results show that the fine-tuning of NFIQ 2 on synthetic contactless fingerprints is a viable alternative to training on real databases. Moreover, the evaluation shows that our MCLFIQ method works more accurately and is more robust compared to all baseline methods on contactless fingerprints. We suggest considering the proposed MCLFIQ method as a starting point for the development of a new standard algorithm for contactless fingerprint quality assessment.
... Many approaches like Lin and Kumar [19] practiced the CNN model with features like minutiae and core points, and Malhotra et al. [20] used deep features. Chowdhury and Imtiaz [21] accomplished a detailed study of the deep learning approaches for contactless fingerprint recognition. They enlightened the basic architecture of the deep learning model and their stages of pre-processing, feature extraction, and matching with its usability and possible downsides. ...
A contactless fingerprint is a novel approach to fingerprint recognition, that originated after the covid-19 pandemic because of hygiene issues. In contactless and contact-based fingerprint matching several patterns and features are used to study the ridge-valley patterns of the fingertip. So, the performance of a fingerprint matching system depends on the feature extraction phase. Therefore, feature selection should be done cautiously. Over the period, numerous features and corresponding approaches have been developed for fingerprint recognition. Selecting one or more features is a crucial step in fingerprint identification, which depends upon the database and type of application (like military, commercial, etc.). Hence, the purpose of this study is to do a comprehensive assessment of the available features for matching contact-based and contactless fingerprints.
... Chowdhury et al. [16] worked with deep learning-based methods and models to benchmark them on the task. Eight additional scientific articles were compared, and they conducted their investigation using three distinct types of methodologies. ...
Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable results in image classification, detection, and many others. The proposed methodology relies on an attention model and ResNet convolutions. Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. The method’s performances have been tested across different pooling strategies, such as Max, Average, and Stochastic, over the LivDet-2021 dataset. Comparisons against different state-of-the-art variants of Convolutional Neural Networks, such as DenseNet121, VGG19, InceptionV3, and conventional ResNet50, have been carried out. In particular, tests have been aimed at assessing ResNet34 and ResNet50 models on feature extraction by further enhancing the sequential attention model. A Multilayer Perceptron (MLP) classifier used alongside a fully connected layer returns the ultimate prediction of the entire stack. Finally, the proposed method is also evaluated on feature extraction with and without attention models for ResNet and considering different pooling strategies.
... However, the latest emerging trends, recent advances and their challenges in fingerprint sensing have been missed. Another recent article [30] restricted the review to deep-learning-based methods in contactless fingerprint recognition. Further, there is no work that explores challenges in fingerprint systems in the aspects of the sensor level and image-acquisition level with the whole range of fingerprints including plain, rolled, latent, partial and contactless 2D and 3D images under a common framework. ...
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.
... In the past years, contactless fingerprint recognition has been introduced as a more convenient alternative to contactbased schemes [1], [2], [3]. In contrast to contact-based capturing schemes where the finger is pressed onto a planar surface, contactless recognition workflows do not require any contact between the subject and the capturing subsystem. ...
We propose MCLFIQ: Mobile Contactless Fingerprint Image Quality, the first quality assessment algorithm for mobile contactless fingerprint samples. To this end, we retrained the NIST Fingerprint Image Quality (NFIQ) 2 method, which was originally designed for contact-based fingerprints, with a synthetic contactless fingerprint database. We evaluate the predictive performance of the resulting MCLFIQ model in terms of Error-vs.-Discard Characteristic (EDC) curves on three real-world contactless fingerprint databases using two recognition algorithms. In experiments, the MCLFIQ method is compared against the original NFIQ 2 method and a sharpness-based quality assessment algorithm developed for contactless fingerprint images. Obtained results show that the re-training of NFIQ 2 on synthetic data is a viable alternative to training on real databases. Moreover, the evaluation shows that our MCLFIQ method works more accurate and robust compared to NFIQ 2 and the sharpness-based quality assessment. We suggest considering the proposed MCLFIQ method as a candidate for a new standard algorithm for contactless fingerprint quality assessment.
... The authors of [10] presented a simple summation on palmprint database acquisition, data preprocessing, and feature representation. Furthermore, ref. [11] summarized the development of contactless fingerprint recognition using deep-learning-based technologies. Compared with the previous literature reviews on palmprint authentication, this paper provides a more comprehensive summary of multiview palmprint recognition, including different types of single-view features, open-set palmprint databases, ROI extraction, multiview feature containers, and multiview learning for feature fusion. ...
... Mathematics 2023,11, 1261 ...
Palmprint recognition has been widely applied to security authentication due to its rich characteristics, i.e., local direction, wrinkle, and texture. However, different types of palmprint images captured from different application scenarios usually contain a variety of dominant features. Specifically, the palmprint recognition performance will be degraded by the interference factors, i.e., noise, rotations, and shadows, while palmprint images are acquired in the open-set environments. Seeking to handle the long-standing interference information in the images, multiview palmprint feature learning has been proposed to enhance the feature expression by exploiting multiple characteristics from diverse views. In this paper, we first introduced six types of palmprint representation methods published from 2004 to 2022, which described the characteristics of palmprints from a single view. Afterward, a number of multiview-learning-based palmprint recognition methods (2004–2022) were listed, which discussed how to achieve better recognition performances by adopting different complementary types of features from multiple views. To date, there is no work to summarize the multiview fusion for different types of palmprint features. In this paper, the aims, frameworks, and related methods of multiview palmprint representation will be summarized in detail.
... The search procedure was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines [17]. There are several studies that follow PRISMA guidelines for systematic review, such as Imtiaz et al. [14], Bougea et al. [18], and Chowdhury et al. [19]. For this review, we adopted their processes. ...
Maternal health includes health during pregnancy and childbirth. Each stage during pregnancy should be a positive experience, ensuring that women and their babies reach their full potential in health and well-being. However, this cannot always be achieved. According to UNFPA (United Nations Population Fund), approximately 800 women die every day from avoidable causes related to pregnancy and childbirth, so it is important to monitor mother and fetal health throughout the pregnancy. Many wearable sensors and devices have been developed to monitor both fetal and the mother’s health and physical activities and reduce risk during pregnancy. Some wearables monitor fetal ECG or heart rate and movement, while others focus on the mother’s health and physical activities. This study presents a systematic review of these analyses. Twelve scientific articles were reviewed to address three research questions oriented to (1) sensors and method of data acquisition; (2) processing methods of the acquired data; and (3) detection of the activities or movements of the fetus or the mother. Based on these findings, we discuss how sensors can help effectively monitor maternal and fetal health during pregnancy. We have observed that most of the wearable sensors were used in a controlled environment. These sensors need more testing in free-living conditions and to be employed for continuous monitoring before being recommended for mass implementation.
... (3) Biometric-based UA methods: Currently, the most widely-used way to authenticate users on wearable devices is the biometric-based UA method [20][21][22][23][24][25][26]. Some biometricbased UA methods are based on face recognition [20][21][22]. ...
... Furthermore, external and environmental artifacts such as face masks, glasses, and illumination sources may interfere with the UA method, which results in lowering the authentication accuracy. Some biometric-UA methods are based on fingerprints [23][24][25][26], which has the limitation that additional hardware is required. ...
User authentication (UA) is the process by which biometric techniques are used by a person to gain access to a physical or virtual site. UA has been implemented in various applications such as financial transactions, data privacy, and access control. Various techniques, such as facial and fingerprint recognition, have been proposed for healthcare monitoring to address biometric recognition problems. Photoplethysmography (PPG) technology is an optical sensing technique which collects volumetric blood change data from the subject’s skin near the fingertips, earlobes, or forehead. PPG signals can be readily acquired from devices such as smartphones, smartwatches, or web cameras. Classical machine learning techniques, such as decision trees, support vector machine (SVM), and k-nearest neighbor (kNN), have been proposed for PPG identification. We developed a UA classification method for smart devices using long short-term memory (LSTM). Specifically, our UA classifier algorithm uses raw signals so as not to lose the specific characteristics of the PPG signal coming from each user’s specific behavior. In the UA context, false positive and false negative rates are crucial. We recruited thirty healthy subjects and used a smartphone to take PPG data. Experimental results show that our Bi-LSTM-based UA algorithm based on the feature-based machine learning and raw data-based deep learning approaches provides 95.0% and 96.7% accuracy, respectively.
