Multibiometric Fusion Authentication in Wireless Multimedia Environment Using Dynamic Bayesian Method

Wu, Zihao; Yang, Jiajia; Zhang, Jianwu; Yue, Hengli

doi:10.1155/2018/5783976

Cited by 4 publications

(7 citation statements)

References 32 publications

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“…In 2017, Moi and Pang proposed a multibiometric authentication framework that combines the iris and human face 11 . In 2018, Wu et al proposed a fingerprint and voice print fusion authentication framework that is based on the dynamic Bayes 12 . In 2020, Gupta et al proposed a new revocable multimodal biometric identification system 13 .…”

Section: Related Workmentioning

confidence: 99%

“…11 In 2018, Wu et al proposed a fingerprint and voice print fusion authentication framework that is based on the dynamic Bayes. 12 In 2020, Gupta et al proposed a new revocable multimodal biometric identification system. 13 By projecting feature points onto a random plane to generate revocable features, this system can achieve a better privacy protection performance by integrating multimodal feature data.…”

Section: Related Workmentioning

confidence: 99%

“…Tables 2 and 3 and Figures [9][10][11][12] show the performance comparison between MCP-FPmodel algorithm and SENet-DNN algorithm. 23 Each algorithm was tested for 20 rounds to eliminate contingency.…”

Section: Mcp-fpmodel Fingerprint Bio-key Generation Performancementioning

confidence: 99%

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Fingerprint bio‐key generation based on a deep neural network

Kang

et al. 2021

Int J of Intelligent Sys

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With the increasing use of biometric identity authentication, biological key generation technology is receiving much attention. A high-strength key that is easy to store and manage can be generated from biological characteristics, which can improve the convenience and security of user-encryption operations. However, the generation of a high-strength, stable, and robust key using the currently available fingerprint bio-key generation technology is difficult. This paper proposes a three-layer framework for fingerprint bio-key generation that is composed of a fingerprint bio-key preprocessor, fingerprint bio-key stabilizer (FPBK_Stabilizer), and fingerprint bio-key fuzzy extractor. In the FPBK_Stabilizer, feature selection and layer-by-layer convolution projection characteristics from deep neural networks are used to effectively eliminate the instability between fingerprint samples. Furthermore, a suitable multilayer convolutional projection fingerprint bio-key generation model is designed for generating the fingerprint bio-key. The results of a fingerprint bio-key generation experiment involving a fingerprint library comprising 100 people verified the efficacy of the proposed framework. Specifically, the proposed framework exhibited a generation intensity >1024 bits, accuracy rate >98.0%, and misrecognition rate <1.5%, thereby verifying its high-strength, stable, and robust fingerprint bio-key generation capability.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Fingerprint bio‐key generation based on a deep neural network

Kang

et al. 2021

Int J of Intelligent Sys

Self Cite

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“…They also proposed a three‐bit quantization technology that adapted to face feature changes and reduced feature size. In 2018, Wu et al 28 proposed a multibiological fusion authentication framework based on dynamic Bayesian decision‐making methods. The core idea was to use matching layer scores to help integrate biological characteristics at the decision‐making level.…”

Section: Related Workmentioning

confidence: 99%

Semantic key generation based on natural language

Kang

Jiang

2021

Int J of Intelligent Sys

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In recent years, the public has become more aware of security concerns, and the demand for convenient and efficient encryption technology has increased. Biological data is used in identity authentication and key generation as the innate characteristic information of people. Biological key have the advantage of convenience and fast application without carrying any document; however, they also have the disadvantage of biological characteristic leaks and the inability to change. Based on the advantages and disadvantages of biological key, we propose the concept of semantic key. Language, a medium that fills the lives of people, has similar characteristics of convenience and fast application as biological key; however, semantic key will not reveal biological information.As the amount of semantic information is large, it can be changed at any time. Compared with biological key, it provides better security and flexibility. Therefore, we propose a semantic key generation framework of semantic extraction + feature stabilization + fuzzy extraction that improves the existing semantic extraction model and feature stabilization model and design the semantic key extraction model. In terms of artificial sentence formation, semantic key can be extracted with an accuracy of more than 99%, and an error rate of less than 0.5%.

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“…In the literature, the studies based on biometric systems have shown that the use of several biometric methods will be necessary to obtain acceptable identification accuracy for an identification application of a large population of users [19]. In any case, it is not necessary for the different measurements to be mathematically combined.…”

Section: Basics and Access Controlmentioning

confidence: 99%

Multimodal access control system combining RFID, fingerprint and facial recognition

Beqqal

Azizi

Lanet

2020

IJEECS

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<span>Monomodal biometry does not constitute an effective measure to meet the desired performance requirements for large-scale applications, due to limita-tions such as noisy data, restricted degree of freedom and unacceptable error rates. Some of these problems can be solved through multimodal biometric systems that involve using a combination of two or more biometric modali-ties in a single identification system. Identification based on multiple biomet-rics represents an emerging trend. The reason for combining different modal-ities is to improve the recognition rate. In practice, multi-biometric aims to reduce the False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) which are two standard metrics widely used in the accuracy of biometric sys-tems. In this paper, we will examine the different possible scenario in multi-modal biometric systems using RFID, fingerprint and facial recognition, that can be adopted to merge information and improve the overall accuracy of the system.</span>

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Multibiometric Fusion Authentication in Wireless Multimedia Environment Using Dynamic Bayesian Method

Cited by 4 publications

References 32 publications

Fingerprint bio‐key generation based on a deep neural network

Fingerprint bio‐key generation based on a deep neural network

Semantic key generation based on natural language

Multimodal access control system combining RFID, fingerprint and facial recognition

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