Double Layers Self-Organized Spiking Neural P Systems With Anti-Spikes for Fingerprint Recognition
| Autor: | Shudong Wang, Shaohua Hao, Tongmao Ma, Alfonso Rodríguez-Patón, Tao Song, Xun Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Artificial neural network Computer science business.industry General Engineering spiking neural P systems Word error rate Pattern recognition Fingerprint recognition self-organization Synapse membrane computing Biological neural network General Materials Science Spike (software development) Artificial intelligence lcsh:Electrical engineering. Electronics. Nuclear engineering business Membrane computing lcsh:TK1-9971 |
| Zdroj: | IEEE Access, Vol 7, Pp 177562-177570 (2019) |
| ISSN: | 2169-3536 |
| Popis: | In this paper, we design a double layers self-organized spiking neural P system with anti-spikes for fingerprint recognition. The system can self-adaptively create and delete synapse between the neurons in different layers and recognize fingerprints by the spike trains emitted out of the output neurons. Data experiments are conducted on FVC2002 and FVC2004 Databases with EER (Equal Error Rate) 9.5% around, and much less parameters are involved in our SN P systems than Capsule Neural Networks. To our best knowledge, it is the first attempt of using SN P systems to do fingerprint recognition, which can also provide theoretical models for spiking neural circuits recognizing fingerprints. |
| Databáze: | OpenAIRE |
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