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

<p>Biometric information is the information of human physiological and behavioural characteristics used for automatic identification, state analysis and attribute estimation. It has the characteristics of uniqueness, stability, easy collection and public interest. Illegal acquisition, provision and use of biometric information may constitute an infringement of others’ personal rights, property rights, public order and even national interests. The civil code clearly expresses the importance and protection of biometric information and distinguishes biometric information from general personal information, but the criminal law has not responded to this in a timely manner. Based on the analysis of the minimum uncertainty neural network (MUNN) model, this article discusses the attribution object of biometric information and gives the path of attribution of criminal law under biometric information. The empirical results show that the number of misjudgements, the recognition rate and the number of iterations of the MUNN model proposed in this study are 65, 93.5% and 1501, respectively, and the effects are all optimal. Due to the insufficient response of the criminal law to biometric information, there are differences in main charges and sentences, and the consistency is 50% and 75%, respectively.</p>
</abstract>

Biometric information is the information of human physiological and behavioural characteristics used for automatic identification, state analysis and attribute estimation. It has the characteristics of uniqueness, stability, easy collection and public interest. Illegal acquisition, provision and use of biometric information may constitute an infringement of others’ personal rights, property rights, public order and even national interests. The civil code clearly expresses the importance and protection of biometric information and distinguishes biometric information from general personal information, but the criminal law has not responded to this in a timely manner. Based on the analysis of the minimum uncertainty neural network (MUNN) model, this article discusses the attribution object of biometric information and gives the path of attribution of criminal law under biometric information. The empirical results show that the number of misjudgements, the recognition rate and the number of iterations of the MUNN model proposed in this study are 65, 93.5% and 1501, respectively, and the effects are all optimal. Due to the insufficient response of the criminal law to biometric information, there are differences in main charges and sentences, and the consistency is 50% and 75%, respectively.

Criminal law imputation path for biometric information

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Criminal law imputation path for biometric information"}

Biometric information is the information of human physiological and behavioural characteristics used for automatic identification, state analysis and attribute...

Criminal law imputation path for biometric information

Published Online: Dec 23, 2022

Page range: 2705 - 2716

Received: Jul 08, 2022

Accepted: Aug 07, 2022

DOI: https://doi.org/10.2478/amns.2021.2.00280

Keywords
Biometric Information, Criminal Law Imputation Path, Face Recognition, Fingerprint Recognition

© 2023 Yifang Wang, published by Sciendo

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

Criminal law imputation path for biometric information

Published Online: Dec 23, 2022

Page range: 2705 - 2716

Received: Jul 08, 2022

Accepted: Aug 07, 2022

DOI: https://doi.org/10.2478/amns.2021.2.00280

KeywordsBiometric Information, Criminal Law Imputation Path, Face Recognition, Fingerprint Recognition

© 2023 Yifang Wang, published by Sciendo

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

Keywords
Biometric Information, Criminal Law Imputation Path, Face Recognition, Fingerprint Recognition