Research on Multimode Biometric Features Recognition System Adopting Neural Network

Wang, Xiaosong; Zheng, Zhiqing

doi:10.2174/1874110x01509012508

Cited by 2 publications

(2 citation statements)

References 14 publications

(12 reference statements)

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“…As we dive deeper into the realm of biometric authentication, it's essential to analyze real-world scenarios where biometric authentication can effectively replace traditional methods like SMS verification [9]. A deeper understanding of these scenarios will shed light on the practical applications of this technology and how it can be effectively deployed to enhance user security and convenience.…”

Section: Research On Biological Validation Application Scenariosmentioning

confidence: 99%

Risk Analysis Research on SMS Verification Code and Biometric Recognition Technology

2024

HSET

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This paper delves into the ongoing transformation in the domain of digital identity security. It specifically focuses on the paradigm shift from traditional methods of authentication, such as passwords and text message verification codes, towards the use of biometric recognition technology. By scrutinizing the inherent weaknesses linked with the utilization of text message verification codes, the paper underlines the demand for more trustworthy and secure solutions for authentication. Following this, biometric authentication is thoroughly examined, with a highlight on its unique features, the convenience it provides to users, and its capacity for ongoing verification. The discussion extends to practical applications of biometrics in a variety of sectors, including healthcare and high-security workplaces, as well as in situations where employing text message verification is unsuitable. A focal point of the discussion is multi-modal biometric authentication. While acknowledging the potential hurdles and future possibilities tied to the adoption of biometric technology, the paper offers an objective perspective. The overall intent of this research is to furnish insights that will enable informed decision-making regarding the deployment and improvement of digital identity verification methods across a wide range of industries.

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Section: Research On Biological Validation Application Scenariosmentioning

confidence: 99%

Risk Analysis Research on SMS Verification Code and Biometric Recognition Technology

2024

HSET

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“…Liu et al [7] the author necessitates the image enhancement required in the biometric traits before subjecting it to the deep learning techniques and utilizes the fuzzy operation to enhance the images, before subjecting into the deep learning methods such as the CNN and the Caps Net. [8] applies the convolutional neural network for the recognition of the finger print, Xiaosong, et al [9] proceeds with the research on the multimode biometric recognition utilizing the neural networks. Akhtar et al [10] the author present the over view of the recent technologies in the face manipulation, generation detection and the recognition data base.…”

Section: Related Workmentioning

confidence: 99%

Capsule Network Based Biometric Recognition System

Jacob¹

2019

JAICN

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The biometric recognition plays a significant and a unique part in the applications that are based on the personal identification. This is because of the stability, irreplaceability and the uniqueness that is found in the biometric traits of the humans. Currently the deep learning techniques that are capable of strongly generalizing and automatically learning, with the enhanced accuracy is utilized for the biometric recognition to develop an efficient biometric system. But the poor noise removal abilities and the accuracy degradation caused due to the very small disturbances has made the conventional means of the deep learning that utilizes the convolutional neural network incompatible for the biometric recognition. So the capsule neural network replaces the CNN due to its high accuracy in the recognition and the classification, due to its learning capacities and the ability to be trained with the limited number of samples compared to the CNN (convolutional neural network). The frame work put forward in the paper utilizes the capsule network with the fuzzified image enhancement for the retina based biometric recognition as it is a highly secure and reliable basis of person identification as it is layered behind the eye and cannot be counterfeited. The method was tested with the dataset of face 95 database and the CASIA-Iris-Thousand, and was found to be 99% accurate with the error rate convergence of 0.3% to .5%

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Research on Multimode Biometric Features Recognition System Adopting Neural Network

Cited by 2 publications

References 14 publications

Risk Analysis Research on SMS Verification Code and Biometric Recognition Technology

Risk Analysis Research on SMS Verification Code and Biometric Recognition Technology

Capsule Network Based Biometric Recognition System

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