Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication
Abstract Stream‐based biometric authentication using a novel approach based on spiking neural networks (SNNs) is addressed. SNNs have proven advantages regarding energy consumption and they are a perfect match with some proposed neuromorphic hardware chips, which can lead to a broader adoption of us...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-09-01
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| Series: | IET Biometrics |
| Online Access: | https://doi.org/10.1049/bme2.12099 |
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| _version_ | 1832559605020884992 |
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| author | Enrique Argones Rúa Tim Vanhamme Davy Preuveneers Wouter Joosen |
| author_facet | Enrique Argones Rúa Tim Vanhamme Davy Preuveneers Wouter Joosen |
| author_sort | Enrique Argones Rúa |
| collection | DOAJ |
| description | Abstract Stream‐based biometric authentication using a novel approach based on spiking neural networks (SNNs) is addressed. SNNs have proven advantages regarding energy consumption and they are a perfect match with some proposed neuromorphic hardware chips, which can lead to a broader adoption of user device applications of artificial intelligence technologies. One of the challenges when using SNNs is the discriminative training of the network since it is not straightforward to apply the well‐known error backpropagation (EBP), massively used in traditional artificial neural networks (ANNs). A network structure based on neuron columns is proposed, resembling cortical columns in the human cortex, and a new derivation of error backpropagation for the spiking neural networks that integrate the lateral inhibition in these structures. The potential of the proposed approach is tested in the task of inertial gait authentication, where gait is quantified as signals from Inertial Measurement Units (IMU), and the authors' approach to state‐of‐the‐art ANNs is compared. In the experiments, SNNs provide competitive results, obtaining a difference of around 1% in half total error rate when compared to state‐of‐the‐art ANNs in the context of IMU‐based gait authentication. |
| format | Article |
| id | doaj-art-1216d63f92a547808f27c26d498181eb |
| institution | Kabale University |
| issn | 2047-4938 2047-4946 |
| language | English |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Biometrics |
| spelling | doaj-art-1216d63f92a547808f27c26d498181eb2025-02-03T01:29:39ZengWileyIET Biometrics2047-49382047-49462022-09-0111548549710.1049/bme2.12099Discriminative training of spiking neural networks organised in columns for stream‐based biometric authenticationEnrique Argones Rúa0Tim Vanhamme1Davy Preuveneers2Wouter Joosen3Department of Electrical Engineering imec—COSIC KU Leuven Heverlee BelgiumDepartment of Computer Science imec—DistriNet KU Leuven Heverlee BelgiumDepartment of Computer Science imec—DistriNet KU Leuven Heverlee BelgiumDepartment of Computer Science imec—DistriNet KU Leuven Heverlee BelgiumAbstract Stream‐based biometric authentication using a novel approach based on spiking neural networks (SNNs) is addressed. SNNs have proven advantages regarding energy consumption and they are a perfect match with some proposed neuromorphic hardware chips, which can lead to a broader adoption of user device applications of artificial intelligence technologies. One of the challenges when using SNNs is the discriminative training of the network since it is not straightforward to apply the well‐known error backpropagation (EBP), massively used in traditional artificial neural networks (ANNs). A network structure based on neuron columns is proposed, resembling cortical columns in the human cortex, and a new derivation of error backpropagation for the spiking neural networks that integrate the lateral inhibition in these structures. The potential of the proposed approach is tested in the task of inertial gait authentication, where gait is quantified as signals from Inertial Measurement Units (IMU), and the authors' approach to state‐of‐the‐art ANNs is compared. In the experiments, SNNs provide competitive results, obtaining a difference of around 1% in half total error rate when compared to state‐of‐the‐art ANNs in the context of IMU‐based gait authentication.https://doi.org/10.1049/bme2.12099 |
| spellingShingle | Enrique Argones Rúa Tim Vanhamme Davy Preuveneers Wouter Joosen Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication IET Biometrics |
| title | Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication |
| title_full | Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication |
| title_fullStr | Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication |
| title_full_unstemmed | Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication |
| title_short | Discriminative training of spiking neural networks organised in columns for stream‐based biometric authentication |
| title_sort | discriminative training of spiking neural networks organised in columns for stream based biometric authentication |
| url | https://doi.org/10.1049/bme2.12099 |
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