<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Identity authentication and identification as the main activities in the modern human daily life, in the development of social economy and technological innovation of science and technology, information security and public security and other fields of identity recognition technology put forward higher request, not only to accurately record the relevant data information, and use of advanced technology concept advanced authentication technology is put forward. Knowing the living habits of current social residents, they need to choose appropriate ways to prove their identity, whether they enter the computer system or participate in financial services. For example, passwords, keys, ID cards and other contents are traditional means of identity authentication. As the reliability of traditional identity authentication means is getting lower and lower in today’s social network environment, in order to meet the needs of increasingly innovative network platforms and enhance the security and reliability of identity authentication technology, researchers propose to use biometric identification, among which face recognition is the most common. This recognition method is more direct and effective than palmprint, retina, fingerprint and so on, and occupies an important position in the current biometric research topic. After understanding the application of hidden Markov model (HMM) based on wavelet transform and its improved model in the field of face authentication, this paper analyzes the influence of various observation vector optimization methods on the authentication probability, and proposes a dynamic threshold method combined with the authentication model, in order to obtain a better authentication probability. The final design and implementation of the face information as the core of the identity authentication system. The improved hidden Markov model can effectively represent the correlation between face organs, so it has a positive role in the application of face modeling and authentication technology.</p>
</abstract>

Identity authentication and identification as the main activities in the modern human daily life, in the development of social economy and technological innovation of science and technology, information security and public security and other fields of identity recognition technology put forward higher request, not only to accurately record the relevant data information, and use of advanced technology concept advanced authentication technology is put forward. Knowing the living habits of current social residents, they need to choose appropriate ways to prove their identity, whether they enter the computer system or participate in financial services. For example, passwords, keys, ID cards and other contents are traditional means of identity authentication. As the reliability of traditional identity authentication means is getting lower and lower in today’s social network environment, in order to meet the needs of increasingly innovative network platforms and enhance the security and reliability of identity authentication technology, researchers propose to use biometric identification, among which face recognition is the most common. This recognition method is more direct and effective than palmprint, retina, fingerprint and so on, and occupies an important position in the current biometric research topic. After understanding the application of hidden Markov model (HMM) based on wavelet transform and its improved model in the field of face authentication, this paper analyzes the influence of various observation vector optimization methods on the authentication probability, and proposes a dynamic threshold method combined with the authentication model, in order to obtain a better authentication probability. The final design and implementation of the face information as the core of the identity authentication system. The improved hidden Markov model can effectively represent the correlation between face organs, so it has a positive role in the application of face modeling and authentication technology.

Research on the effect of generative adversarial network based on wavelet transform hidden Markov model on face creation and classification

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Identity authentication and identification as the main activities in the modern human daily life, in the development of social economy and technological...

Research on the effect of generative adversarial network based on wavelet transform hidden Markov model on face creation and classification

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 821 - 830

Eingereicht: 06. Feb. 2022

Akzeptiert: 16. Apr. 2022

DOI: https://doi.org/10.2478/amns.2022.2.0069

Schlüsselwörter
Wavelet transform, Hidden Markov model, Face authentication, Feature, extraction

© 2023 Yanjiang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

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Research on the effect of generative adversarial network based on wavelet transform hidden Markov model on face creation and classification

Online veröffentlicht: 15. Juli 2022

Seitenbereich: 821 - 830

Eingereicht: 06. Feb. 2022

Akzeptiert: 16. Apr. 2022

DOI: https://doi.org/10.2478/amns.2022.2.0069

SchlüsselwörterWavelet transform, Hidden Markov model, Face authentication, Feature, extraction

© 2023 Yanjiang Li et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure. 1

Figure. 2

Figure. 3

Figure 4

Figure. 5

Figure 6

Figure 7

Figure. 8

Figure 9

Schlüsselwörter
Wavelet transform, Hidden Markov model, Face authentication, Feature, extraction