DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Tarawneh, Ahmad S.; Hassanat, Ahmad B. A.; Alkafaween, Esra’a; Sarayrah, Bayan; Mnasri, Sami; Altarawneh, Ghada A.; Alrashidi, Malek; Alghamdi, Mansoor; Almuhaimeed, Abdullah

doi:10.3390/electronics11040513

Cited by 23 publications

(4 citation statements)

References 138 publications

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“…Finally, optimize the mobility models to ensure they accurately reflect the movement of sinks in realistic WSN applications with QoS constraints. The proposed model may also be applied to numerous real-world engineering problems [61,62].…”

Section: Discussionmentioning

confidence: 99%

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks

Ben Yagouta,

Ben Gouissem,

Mnasri

et al. 2023

Sensors

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The involvement of wireless sensor networks in large-scale real-time applications is exponentially growing. These applications can range from hazardous area supervision to military applications. In such critical contexts, the simultaneous improvement of the quality of service and the network lifetime represents a big challenge. To meet these requirements, using multiple mobile sinks can be a key solution to accommodate the variations that may affect the network. Recent studies were based on predefined mobility models for sinks and relied on multi-hop routing techniques. Besides, most of these studies focused only on improving energy consumption without considering QoS metrics. In this paper, multiple mobile sinks with random mobile models are used to establish a tradeoff between power consumption and the quality of service. The simulation results show that using hierarchical data routing with random mobile sinks represents an efficient method to balance the distribution of the energy levels of nodes and to reduce the overall power consumption. Moreover, it is proven that the proposed routing methods allow for minimizing the latency of the transmitted data, increasing the reliability, and improving the throughput of the received data compared to recent works, which are based on predefined trajectories of mobile sinks and multi-hop architectures.

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Section: Discussionmentioning

confidence: 99%

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks

Ben Yagouta,

Ben Gouissem,

Mnasri

et al. 2023

Sensors

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“…Morales et al [10] applied a Gabor channel to improve the FKP data and a scale-invariant component change (filter) to extricate the elements. Tarawneh et al [11] give an FKP framework in light of the VGG-19 profound model to remove profound highlights from FKP pictures. The removed highlights are gathered from the VGG-19 model's layer 6 and layer 7.…”

Section: -Related Workmentioning

confidence: 99%

Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Al-juboori

Abed

2022

Bulletin EEI

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Biometric-based individual distinguishing proof is a successful strategy for consequently perceiving, with high certainty, an individual's character. The utilization of finger knuckle pictures for individual ID has shown promising outcomes and produced a ton of interest in biometrics. By seeing that the surface example delivered by twisting the finger knuckle is profoundly particular, in this paper we present a new biometric validation framework utilizing finger-knuckle-print (FKP) imaging. In this paper, another methodology in view of neighborhood surface examples is proposed. Local derivative pattern (LDP) histogram is investigated for FKP description. Then based on neighborhood preserving embedding (NPE) is used for dimension reduction to the feature vector. The feature extraction method is computed and evaluated in the identification framework task. The machine learning methods (multiclass support vector machine (MSVM), random forest (RF), k-nearest neighbor (KNN)) are proposed for classification. The system is tested on the PolyU finger knuckle database. The empirical results proved that the proposed model has the best results with RF. Moreover, our proposed LDP-NPE model has been evaluated and the results show remarkable efficiency in comparison with previous work. Experimentally, the proposed model has better accuracy as reflected by 99.65%.

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“…Another useful feature of smartphones is their capacity to support electronic payments, making mobile transactions and financial transactions more accessible [10]. Smartphones incorporate biometric authentication methods such as facial recognition [11], fingerprint identification [12,13], and palm-print identification [14,15], which are used not only for user recognition, but also for specialized applications such as identifying potential threats or terrorists [16,17].…”

Section: Introductionmentioning

confidence: 99%

Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data

Al-Mahadeen,

Alghamdi,

Tarawneh

et al. 2023

Sustainability

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With the increasing popularity of smartphones, user identification has become a critical component to ensure security and privacy. This study looked into how smartphone sensors’ data can be utilized to identify/authenticate users and gives suggestions for the best application components. A public smartphone dataset was used to train a deep learning algorithms, conventional classifiers, and voting classifiers, which were then used to identify the users. Feature selection and Pre-processing techniques were investigated to improve the performance. According to the results, Recursive Feature Elimination beat the other feature-selection approaches, and Long Short-Term Memory (LSTM) had the best identification performance, as evidenced by a relatively large number of machine learning performance metrics. Even with a larger number of users, the proposed identification system performed well and outperformed existing approaches, which were primarily designed and tested on the same public smartphone dataset. In terms of user authentication, this study compared the effectiveness of accelerometer data against gyroscope data. According to the findings, the accelerometer data surpassed the gyroscope data in the authentication process. Notably, the study revealed that employing LSTM to combine the accelerometer and gyroscope data resulted in near-perfect user authentication. The insights gained from this study help to develop user identification and authentication approaches that employ smartphone accelerometer data.

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DeepKnuckle: Deep Learning for Finger Knuckle Print Recognition

Cited by 23 publications

References 138 publications

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks

Multiple Mobile Sinks for Quality of Service Improvement in Large-Scale Wireless Sensor Networks

Finger knuckle pattern person identification system based on LDP-NPE and machine learning methods

Smartphone User Identification/Authentication Using Accelerometer and Gyroscope Data

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