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نتیجه جستجو - Biometrics

تعداد مقالات یافته شده: 109
ردیف عنوان نوع
21 An easy-to-explain decision support framework for forensic analysis of dynamic signatures
یک چارچوب پشتیبانی تصمیم آسان برای تجزیه و تحلیل پزشکی قانونی امضاهای پویا-2021
Forensic handwriting examination is often criticized for its lack of objective standards and rigorous scientific validation. On the other hand, cutting-edge techniques for biometric handwriting and signature verification are often perceived as perfect black boxes and are not used by forensic handwriting examiners in their work environment. This paper presents an easy-to-explain yet effective framework to support semi-automatic signature verification in forensic settings. The proposed approach is based on measuring similarities between signatures by applying Dynamic Time Warping on easy-to-derive dynamic features. The goal is to provide forensic handwriting examiners with a decision support tool for making reproducible and less questionable inferences, while being both intuitive and easy to explain. The method is tested on a newly proposed dataset that also takes into account the so-called disguised sig- natures which are of extreme importance in this scenario.© 2021 Elsevier Ltd. All rights reserved.
Keywords: Dynamic signatures | Forensic handwriting examination | Behavioral biometrics | Decision support system | Disguised signatures
مقاله انگلیسی
22 The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions
انقلاب آرام در بینایی ماشین-مقاله ای پیشرفته مروری، شامل مرور تاریخی ، چشم اندازها و جهت های آینده-2021
Over the past few years, what might not unreasonably be described as a true revolution has taken place in the field of machine vision, radically altering the way many things had previously been done and offering new and exciting opportunities for those able to quickly embrace and master the new techniques. Rapid developments in machine learning, largely enabled by faster GPU-equipped computing hardware, has facilitated an explosion of machine vision applications into hitherto extremely challenging or, in many cases, previously impossible to automate industrial tasks. Together with developments towards an internet of things and the availability of big data, these form key components of what many consider to be the fourth industrial revolution. This transformation has dramatically improved the efficacy of some existing machine vision activities, such as in manufacturing (e.g. inspection for quality control and quality assurance), security (e.g. facial biometrics) and in medicine (e.g. detecting cancers), while in other cases has opened up completely new areas of use, such as in agriculture and construction (as well as in the existing domains of manufacturing and medicine). Here we will explore the history and nature of this change, what underlies it, what enables it, and the impact it has had - the latter by reviewing several recent indicative applications described in the research literature. We will also consider the continuing role that traditional or classical machine vision might still play. Finally, the key future challenges and developing opportunities in machine vision will also be discussed.‌© 2021 Elsevier B.V. All rights reserved.
Keywords: Machine vision | Machine learning | Deep learning | State-of-the-art
مقاله انگلیسی
23 A novel biometric recognition method based on multi kernelled bijection octal pattern using gait sound
یک روش جدید بیومتریک شناختی مبتنی بر الگوی هشت جداره چند هسته ای با استفاده از صدای راه رفتن-2021
Background: Many gait based methods have been presented about biometric identification in the literature. Gait recognition methods have generally used images and sensors signals. In this work, a novel gait based biometric recognition method is presented. A novel Multi Kernelled Bijection Octal Pattern (MK- BOP) is presented in this study. Object: The main aim of the proposed MK-BOP is to extract distinctive and comprehensive features from a signal (gait sound). By using the proposed MK-BOP, a novel biometric recognition method is proposed. Gait sounds are collected, and two novel datasets are collected. The first dataset is a noisy and heterogeneous dataset. The second dataset is a clear and homogenous dataset. A multileveled method is presented to authenticate subjects from these datasets. One dimensional discrete wavelet transform (1D-DWT) is applied to sound signal with Symlet 6 (sym6) filter, and levels are calculated. Conclusion: The proposed MK-BOP generates features from each level signals, and the generated features are concatenated. A hybrid feature selector (RFNCA) selects the most discriminative feature, and selected most discriminative features are forwarded to classifiers. 0.980 and 0.949 success rates were achieved for clear and noisy datasets, respectively.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Gait recognition | Biometrics | Multi kernelled bijection octal pattern | Information fusion | Sound recognition
مقاله انگلیسی
24 PlexNet: A fast and robust ECG biometric system for human recognition
PlexNet: یک سیستم بیومتریک ECG سریع و قوی برای تشخیص انسان-2021
Researchers have explored the potential of electrocardiogram (ECG) to use as biometrics from past two decades. ECG has the inherent feature of vitality for securing the biometric system from fraudulent attacks. This paper proposes a novel ensemble of the state-of-the- art pre-trained deep neural networks i.e., ResNet and DenseNet for ECG biometric recognition. The principle of transfer learning is utilized to prepare fine-tuned models. The gathered knowledge of four fine-tuned models is fused to prepare one stacking model i.e., ‘PlexNet’. The PlexNet takes advantage of transfer learning along with ensemble learning, thus making a novel model for ECG biometrics that is robust and secure than other met ods using deep networks. Two public datasets PTB and CYBHI are tested on the proposed ensemble for human identification. The experimental results demonstrate the efficacy of the model with identification accuracy reported the best as 99.66% on healthy and unhealthy subjects. Finally, the proposed ECG biometric method proves its robustness from signal acquisition methods, size of datasets, and subject health statuses.© 2021 Elsevier Inc. All rights reserved.
Keywords: Biometric Identification | Electrocardiogram | PlexNet | ResNet | DenseNet
مقاله انگلیسی
25 Jointly learning multi-instance hand-based biometric descriptor
توصیف کننده بیومتریک مبتنی بر دست چند نمونه ای را یادگیری ارتباطی-2021
Multibiometric recognition has become one of the most important solutions for enhancing overall personal recognition performance due to several inherent limitations of unimodal biometrics, such as nonuniversality and unacceptable reliability. However, most existing multibiometrics fuse completely different biometric traits based on addition schemes, which usually require several sensors and make the final feature sets large. In this paper, we propose a joint multi-instance hand-based biometric feature learning method for bio- metric recognition. Specifically, we first exploit the important direction data from multi- instance biometric images. Then, we simultaneously learn the discriminative features of multi-instance biometric traits and exploit the collaborative representations of multi- instance biometric features such that the final joint multi-instance feature descriptor is compact. Moreover, the importance weights of different biometric instances can be adaptively learned. Experimental results on the baseline multi-instance finger-knuckle-print and palmprint databases demonstrate the promising effectiveness of the proposed method.© 2021 Elsevier Inc. All rights reserved.
Keywords: Multibiometrics | Multi-instance biometric recognition | Joint feature learning | Compact feature representation
مقاله انگلیسی
26 Multimodal biometric authentication for mobile edge computing
Multimodal biometric authentication for mobile edge computing-2021
In this paper, we describe a novel Privacy Preserving Biometric Authentication (PPBA) sys- tem designed for Mobile Edge Computing (MEC) and multimodal biometrics. We focus on hill climbing attacks that reveal biometric templates to insider adversaries despite the encrypted storage in the cloud. First, we present an impossibility result on the existence of two-party PPBA systems that are resistant to these attacks. To overcome this negative result, we add a non-colluding edge server for detecting hill climbing attacks both in semi-honest and malicious model. The edge server that stores each user’s secret parameters enables to outsource the biometric database to the cloud and perform matching in the encrypted domain. The proposed system combines Set Overlap and Euclidean Distance metrics using score level fusion. Here, both the cloud and edge servers cannot learn the fused matching score. Moreover, the edge server is prevented from accessing any partial score. The efficiency of the crypto-primitives employed for each biometric modality results in linear computation and communication overhead. Under different MEC scenarios, the new system is found to be most efficient with a 2-tier architecture, which achieves %75 lower latency compared to mobile cloud computing.© 2021 Elsevier Inc. All rights reserved.
Keywords: Privacy Preserving Biometric Authentication (PPBA) | Mobile Edge Computing (MEC) | Multimodal Biometrics | Hill Climbing Attacks (HCA) | Euclidean distance | Malicious security
مقاله انگلیسی
27 Caloric restriction prevents alveolar bone loss in the experimental periodontitis in obese rats
محدودیت کالری جلوگیری از تحلیل استخوان آلوئولار در پریودنتیت آزمایشی در موشهای چاق-2021
Aims: It has been shown that periodontitis, can be modified by systemic changes, including behavioral factors, such as diet. Caloric restriction is one of the dietary therapeutic strategies indicated for obesity. It is associated with several benefits, among them, modulation of the inflammatory response. The aim of this study was to verify whether caloric restriction in obese rats changes the progression of experimental ligature-induced periodontitis. Materials and methods: Forty-eight Wistar rats were used for 24 weeks and initially fed with cafeteria diet during 12 weeks. The animals were divided into four groups according to the caloric restriction and experimental periodontitis. The cotton thread was placed around the mandibular first molars, for 15 days, before the end of the experiment. Rats submitted to caloric restriction received, from the 13th week of the experimental protocol, 70% of the food intake compared to the ad libitum animals of other study of the our research group. Alveolar bone loss was assessed using macroscopic morphometric analysis. Analyzes of clinical periodontal measures, biometrics, serum biomarkers and biochemical parameters were performed. Key findings: Caloric restriction decreased the alveolar bone loss in the periodontitis group when compared to the group that received a cafeteria diet with periodontitis. Moreover, the results demonstrate the improvement in the glycemic profile, without prejudice to bone tissue biomarkers. Significance: Based on the results, caloric restriction reduces the progression of alveolar bone loss in rats with experimental periodontitis, in addition to presenting benefits in biometric data, decreasing both glycemic profile and clinical periodontal measures.
Keywords: Obesity | Caloric restriction | Periodontitis
مقاله انگلیسی
28 Joint discriminative feature learning for multimodal finger recognition
یادگیری ویژگی های تبعیض آمیز مشترک برای تشخیص انگشتان چند حالته-2021
Recently, finger-based multimodal biometrics, due to its high security and stability, has received considerable attention compared with unimodal biometrics. However, existing multimodal finger feature ex- traction approaches separately extract the features of different modalities, at the same time ignoring correlations among these different modalities. Furthermore, most of the conventional finger feature representation approaches are hand-crafted by design, which require strong prior knowledge. It is therefore very important to explore and develop a suitable feature representation and fusion strategy for mul- timodal biometrics recognition. In this paper, we proposed a joint discriminative feature learning (JDFL) framework for multimodal finger recognition by combining finger vein (FV) and finger knuckle print (FKP) patterns. For the FV and FKP images, we first established the informative dominant direction vector by convoluting a bank of Gabor filters and the original finger image. Then, we developed a simple yet effective feature learning algorithm, which simultaneously maximized the distance of between-class samples and minimized the distance of within-class samples, as well as maximized the correlation among inter- modality samples of the within-class. Finally, we integrated the block-wise histograms of the learned feature maps together for multimodal finger fusion recognition. Experimental results demonstrated that the proposed approach has a better recognition performance than state-of-the-art finger recognition methods.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Multimodal biometrics | Feature fusion | Inter-modality | Joint feature learning
مقاله انگلیسی
29 Alignment-free cancelable fingerprint templates with dual protection
الگوهای اثر انگشت قابل انعطاف بدون تراز با محافظت دوگانه-2021
Cancelable fingerprint templates Discrete wavelet transform Attacks via record multiplicity Cancelable biometrics is an important biometric template protection technique. However, many existing cancelable fingerprint templates suffer post-transformation performance deterioration and the attacks via record multiplicity (ARM). In this paper, we design alignment-free cancelable fingerprint templates with dual protection, which is composed of the window-shift-XOR model and the partial discrete wavelet transform. The former defuses the ARM threat and is combined with the latter to provide dual protection and enhance matching performance. The designed cancelable templates meet the requirements of non-invertibility, diversity and revocability and demonstrate superior recognition accuracy, when evaluated over public databases; for example, the Equal Error Rate of the proposed method in the lost-key scenario under the 1vs1 protocol is 0% for both FVC2002 DB1 and DB2, 1.63% for FVC2002 DB3, 7.35% for FVC2004 DB1 and 4.69% for FVC2004DB2.© 2020 Elsevier Ltd. All rights reserved.
Keywords: Cancelable biometrics | Alignment-free | Cancelable fingerprint templates | Discrete wavelet transform | Attacks via record multiplicity
مقاله انگلیسی
30 Jointly learning compact multi-view hash codes for few-shot FKP recognition
کدهای هش چند نمایه ای فشرده یاگیری پیوسته برای تشخیص چند شاتی FKP-2021
As a relatively new biometric trait, Finger-Knuckle-Print (FKP) plays a vital role in establishing a personal authentication system in modern society due to its rich discriminative features, low time cost in image capture and user-friendliness. However, most existing KFP descriptors are hand-crafted and fail to work well with limited training samples. In this paper, we propose a feature learning method for few-shot FKP recognition by jointly learning compact multi-view hash codes (JLCMHC) of a FKP image. We first form the multi-view data vectors (MVDV) to exploit the multiple feature-specific information from a FKP image. Then, we learn a feature projection to encode the MVDV into compact binary codes in an unsupervised manner, where 1) the variance of the learned feature codes on each view is maximized and 2) the difference of the inter-view binary codes is enlarged, so that the redundant information in MVDV is reduced and more informative features can be obtained. Lastly, we pool the binary codes into block-wise statistics features as the final descriptor for FKP representation and recognition. Experimental results on the existing benchmark FKP databases clearly show that the JLCMHC method outperforms the state-of-the-art FKP descriptors.© 2021 Elsevier Ltd. All rights reserved.
Keywords: FKP biometrics | Multi-view features jointly learning | Few-show learning | Compact FKP descriptor
مقاله انگلیسی
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