A deep biometric hash learning framework for three advanced hand‐based biometrics

Shao, Huikai; Zhong, Dexing; Du, Xuefeng

doi:10.1049/bme2.12014

Cited by 9 publications

(2 citation statements)

References 67 publications

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“…The model under consideration demonstrates enhanced performance and yields optimal outcomes to Equal Error Rate (EER). Shao et al [16] designed a deep biometric hash learning (DBHL) model to accurately analyze and manage Palm print, palm vein, and DHV recognitions. In order to transform binary codes from images, implementing a comprehensive network from start to finish is executed.…”

Section: Related Workmentioning

confidence: 99%

Improved Unsupervised Deep Boltzmann Learning Approach for Accurate Hand Vein Recognition

Nour,

Moustafa,

Abdelhay

et al. 2024

IEEE Access

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Dorsal hand vein (DHV) recognition is a burgeoning biometric technology that has recently garnered considerable attention. This article uses image processing and deep learning to present a novel DHV recognition approach. It involves detecting and identifying the unique patterns present in the DHV. The proposed system begins with the preprocessing mechanism that is applied to enhance the quality of the acquired images, including contrast enhancement and noise reduction, by using some filters such as Median and Contrast Limited Adaptive Histogram Equalization (CLAHE). Next, a deep learning model, such as a convolutional neural network (CNN), is employed to automatically abstract discriminative features from the preprocessed vein images. The empirical outcomes prove the influence and reliability of the proposed technique for vein recognition, making it a promising solution for biometric authentication systems. Compared with traditional CNN, the proposed approach shows good accuracy and classification rate results. The suggested model achieved a high recognition rate accuracy, recall, precious, and f-score of 99.7%,97%,96%, and 96%, respectively, and a recognition time of about 1283.45 s. To enrich the model's capability for feature recognition and reduce recognition time, decrease the intricacy of learning and the connectivity CNN structure, an alternative approach based on Restricted Boltzmann Machines (RBM) was assessed. This strategy exhibits superior accuracy in comparison to other contemporary algorithms. The proposed RBM achieved a high recognition rate accuracy, recall, precious, and f-score of 99.9%,99%,99%, and 99%, respectively, and a recognition time of about 137.235s.

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Section: Related Workmentioning

confidence: 99%

Improved Unsupervised Deep Boltzmann Learning Approach for Accurate Hand Vein Recognition

Nour,

Moustafa,

Abdelhay

et al. 2024

IEEE Access

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“…There is a growing need to learn more about the complexities of diabetes [4] as its incidence increases across the globe. Diabetes has a significant influence on public health, and tries to shed light on its many facets by examining its many origins, symptoms, diagnostic tools, and treatment options [5] varieties of DiabeteType 1, Type 2, and gestational diabetes are the three most common forms of diabetes [6]. Origin, treatment, and potential consequences vary greatly amongst the various forms.…”

Section: Introductionmentioning

confidence: 99%

Fine-Tunining the Future: Optimizing svm hyper-parameters or enhanced diabetes prediction

Bommala,

Vamshi Krishna,

Supriya

et al. 2024

MATEC Web Conf.

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Millions of people throughout the globe suffer from diabetes mellitus, a debilitating illness that increases the risk of severe complications and early death. To take preventative measures and tailor treatment to each individual's needs, it is essential to identify diabetes early and estimate risk accurately. This research provides a data-driven strategy for predicting diabetes based on SVM models. This work uses a large dataset, including clinical and demographic data from a wide range of people, including those with and without diabetes, to conduct our analysis. A prediction model that divides people into diabetes and non-diabetic groups based on their input attributes is constructed using the SVM algorithm. Engineers use feature selection and other engineering methods to improve the model's efficacy and readability. The results of the research show that the SVM algorithm is capable of producing reliable predictions of diabetes risk. Measures of the model's efficacy include its sensitivity to false positives, specificity in identifying true positives, and area under the Receiver Operating Characteristics curve (AUC-ROC). In addition, feature significance analysis improves the model's interpretability by illuminating the most critical risk variables for diabetes. The accuracy and interpretability of the proposed SVM-based diabetic prediction model are promising, making it a valuable tool for healthcare practitioners and policymakers to identify those at high risk of developing diabetes and modify preventative measures and interventions appropriately.

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Dual Mode Near-Infrared Scanner for Imaging Dorsal Hand Veins

Zhang¹,

Cui²,

Liu³

et al. 2022

Lecture Notes in Computer Science

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A deep biometric hash learning framework for three advanced hand‐based biometrics

Cited by 9 publications

References 67 publications

Improved Unsupervised Deep Boltzmann Learning Approach for Accurate Hand Vein Recognition

Improved Unsupervised Deep Boltzmann Learning Approach for Accurate Hand Vein Recognition

Fine-Tunining the Future: Optimizing svm hyper-parameters or enhanced diabetes prediction

Dual Mode Near-Infrared Scanner for Imaging Dorsal Hand Veins

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