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Triboelectric biometric signature

Zhang, Renyun ; Hummelgård, Magnus ; Örtegren, Jonas ; Andersson, Henrik LU ; Blomquist, Nicklas ; Phadatare, Manisha ; Patil, Rohan ; Rastabi, Shahrzad Arshadi ; An, Siwen LU and Balliu, Enkeleda , et al. (2022) In Nano Energy 100.
Abstract

Biometric signatures based on either the physiological or behavioural features of a person have been widely used for identification and authentication. However, few strategies have been developed that combine the two types of features in one signature. Here, we report a type of biometric signature based on the triboelectricity of the human body (TEHB) that combines these two types of features. This triboelectric biometric signature (TEBS) can be accomplished by anyone regardless of the physical condition, as it can be performed by many parts of the body. Different TEBS can be identified using a convolutional neural network (CNN) model with a test accuracy of up to 1.0. The TEBS has been further used for text encryption and decryption... (More)

Biometric signatures based on either the physiological or behavioural features of a person have been widely used for identification and authentication. However, few strategies have been developed that combine the two types of features in one signature. Here, we report a type of biometric signature based on the triboelectricity of the human body (TEHB) that combines these two types of features. This triboelectric biometric signature (TEBS) can be accomplished by anyone regardless of the physical condition, as it can be performed by many parts of the body. Different TEBS can be identified using a convolutional neural network (CNN) model with a test accuracy of up to 1.0. The TEBS has been further used for text encryption and decryption with a high sensitivity to changes. Moreover, a dual signed digital signature for enhanced security has been proposed. Our findings provide a new type of TEBS that can be generally used and demonstrated in applications.

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publishing date
type
Contribution to journal
publication status
published
subject
keywords
Biometric signatures, Digital signatures, Encryption and decryption, Human body, Triboelectricity
in
Nano Energy
volume
100
article number
107496
publisher
Elsevier
external identifiers
  • scopus:85132393797
ISSN
2211-2855
DOI
10.1016/j.nanoen.2022.107496
language
English
LU publication?
no
additional info
Publisher Copyright: © 2022 The Authors
id
baf13bbe-1aff-42e3-89ac-695258e87f28
date added to LUP
2023-01-26 16:38:18
date last changed
2023-01-27 11:41:06
@article{baf13bbe-1aff-42e3-89ac-695258e87f28,
  abstract     = {{<p>Biometric signatures based on either the physiological or behavioural features of a person have been widely used for identification and authentication. However, few strategies have been developed that combine the two types of features in one signature. Here, we report a type of biometric signature based on the triboelectricity of the human body (TEHB) that combines these two types of features. This triboelectric biometric signature (TEBS) can be accomplished by anyone regardless of the physical condition, as it can be performed by many parts of the body. Different TEBS can be identified using a convolutional neural network (CNN) model with a test accuracy of up to 1.0. The TEBS has been further used for text encryption and decryption with a high sensitivity to changes. Moreover, a dual signed digital signature for enhanced security has been proposed. Our findings provide a new type of TEBS that can be generally used and demonstrated in applications.</p>}},
  author       = {{Zhang, Renyun and Hummelgård, Magnus and Örtegren, Jonas and Andersson, Henrik and Blomquist, Nicklas and Phadatare, Manisha and Patil, Rohan and Rastabi, Shahrzad Arshadi and An, Siwen and Balliu, Enkeleda and Olin, Håkan}},
  issn         = {{2211-2855}},
  keywords     = {{Biometric signatures; Digital signatures; Encryption and decryption; Human body; Triboelectricity}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Nano Energy}},
  title        = {{Triboelectric biometric signature}},
  url          = {{http://dx.doi.org/10.1016/j.nanoen.2022.107496}},
  doi          = {{10.1016/j.nanoen.2022.107496}},
  volume       = {{100}},
  year         = {{2022}},
}