A Systematic Review on Physiological-Based Biometric Recognition Systems: Current and Future Trends

Shaheed, Kashif; Mao, Aihua; Qureshi, Imran; Kumar, Munish; Abbas, Qaisar; Ullah, Inam; Zhang, Xingming

doi:10.1007/s11831-021-09560-3

Cited by 46 publications

(18 citation statements)

References 193 publications

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“…The MFO procedure is a 3-tuple that approximate the global optima of the optimization problems. 22 MFO = (R, P, T), (6) where R denotes the function of random the population of moths which is obtained by matrix P as represented as…”

Section: Cascade Ff Neural Networkmentioning

confidence: 99%

“…The DL methods gave much enhancement than older finger vein methods. FVR methods were employed in this research paper 6 …”

Section: Literature Surveymentioning

confidence: 99%

“…FVR methods were employed in this research paper. 6 Kashif Shaheed et al evaluated the architecture, performance metrics. The preprocessing, feature extraction, and classification are explored in aspect.…”

mentioning

confidence: 99%

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A secure finger vein verification and authentication scheme for banking network

D¹,

Dhanapal

2022

Concurrency and Computation

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SummaryThe development of electronic exchanges has created a more important interest in fast and accurate customer IDs and verification. Structures, bank records, and PC framework often use personally identifiable access code IDs and test numbers (PINs) for reliable status. Biometric verification requires a level of security, unchanged structure, spoofing, and reliability. In this research work, a biometric authentication system was developed to provide security for access to the ATM network. Honest technology used for security purposes to provide efficient ATM network security is a finger vein. This method undergoes image processing technology such as preprocessing, feature extraction, and classification to locate the finger vein region for accessing the ATM network. Finally, the cascade neural network acts as a classifier method to differentiate registered and unregistered user. The performance analysis of this method is simulated by MATLAB and the process is evaluated to measure the efficient security of the network. The dataset is segregated into two stages; one for training and other for testing. The features extracted with the help of hidden layers with neurons. The matched finger vein is compared to check the accuracy.

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Section: Cascade Ff Neural Networkmentioning

confidence: 99%

“…The DL methods gave much enhancement than older finger vein methods. FVR methods were employed in this research paper 6 …”

Section: Literature Surveymentioning

confidence: 99%

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A secure finger vein verification and authentication scheme for banking network

D¹,

Dhanapal

2022

Concurrency and Computation

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“…e wavelet transform indicates the variation of the signal at various scales, also known as wavelet coefficients. After the image has undergone two onedimensional discrete wavelet transforms, the energy in the high-frequency part is lower and most of the energy concentrated in the low-frequency part [18]. In general, it can be assumed that the wavelet coefficients with relatively large absolute amplitude have their energy provided by the real signal of the image, while the wavelet coefficients with smaller absolute amplitude are provided by external noise.…”

Section: Experimental Design For the Restoration And Fusion Ofmentioning

confidence: 99%

A Markov Model-Based Fusion Algorithm for Distorted Electronic Technology Archives

Wang

2022

Computational Intelligence and Neuroscience

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This paper presents an in-depth study and analysis of the restoration of distorted electronic technology archives using Markov models and proposes a corresponding fusion algorithm. Using the image gradient parametrization as a regular term, the filtering restoration process is constrained and the fuzzy kernel is estimated to solve the degradation problem existing in the Tibetan antiquarian literature. In the algorithmic framework of nonlocal mean filtering, the calculation of the weight function is improved to reduce the computational effort. In the simulation results, it is shown that the improved nonlocal mean filtering restoration algorithm in this paper has good overall quality evaluation performance in the restoration of text-based images. The structural similarity between the generated image and the real image is guaranteed, and the internal mixed-mode learning of a single SAR image is performed by combining the pyramidal hierarchical network structure to improve the effectiveness of the generated image in general. The method further improves the similarity between the generated and real images, while improving the accuracy of the classification based on the data expanded by the generation method. The feasibility and accuracy of the online algorithm for the parameter estimation problem of the model are illustrated through numerical experiments with two specific examples of hidden Markov models, namely, a double Gaussian mixture model and a finite-state Markov chain model with Gaussian noise. At the same time, the advantages of the algorithm proposed in this paper are demonstrated by comparing the experimental results of the online EM algorithm with those of the offline EM algorithm on a unified model. Finally, the empirical analysis is used to illustrate the application of the algorithm in practical scenarios.

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“…In recent years, with the global COVID-19 pandemic, serious international health security issues have emerged, and there is an urgent need for a convenient and secure identification method. As a second-generation biometric technology, finger vein recognition has gained wide attention due to its high level of convenience and security [ 5 ]. It is noteworthy that noncontact and unconstrained finger vein recognition technology can achieve high recognition accuracy.…”

Section: Introductionmentioning

confidence: 99%

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity

Wang

Liu

et al. 2022

Journal of Healthcare Engineering

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Region of interest (ROI) extraction is a key step in finger vein recognition preprocessing. The current method takes the finger region in the vein image as the ROI, but this method cannot obtain better recognition accuracy because it only removes the background noise of the image and ignores factors such as the position and shape of the finger. To solve this problem, we limited the ROI to a fixed region between two finger joint cavities, proposed a new large receptive field gradient operator, and designed and implemented a new method for ROI extraction. It uses a large receptive field to search the target, which is similar to human vision, thus solving the problem of difficult ROI localization for images with large gradient areas. Moreover, for external factors such as noise and uneven illumination in the vein image, the interference factors can be eliminated by averaging them to a larger range with a larger size operator to improve the accuracy of the subsequent matching recognition. To verify the effectiveness of the proposed method, we conducted sufficient matching experiments on three public finger vein datasets. On various datasets, our method significantly reduced the identified EER value, with the lowest EER value reaching 0.96%. The experimental results show that the proposed ROI extraction method can effectively eliminate the influence of finger position, finger shape, and other factors on the subsequent recognition performance, accurately locate the finger joint cavity, and effectively improve the recognition performance.

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A Systematic Review on Physiological-Based Biometric Recognition Systems: Current and Future Trends

Cited by 46 publications

References 193 publications

A secure finger vein verification and authentication scheme for banking network

A secure finger vein verification and authentication scheme for banking network

A Markov Model-Based Fusion Algorithm for Distorted Electronic Technology Archives

An ROI Extraction Method of Finger Vein Images Based on Large Receptive Field Gradient Operator for Accurate Localization of Joint Cavity

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