A human vision based system for biometric images recognition
In this paper, a universal biometric system based on human vision is proposed. From recent biological and physiological results, A human identification system that approximates the natural vision and recognition of individuals is conceived. Liquid state machine (LSM), as a recurrent spiking neural n...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2022-03-01.
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| Online Access: | Get fulltext |
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| LEADER | 02456 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs27247_16097 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Boukhari, Wassila |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Benyettou, Mohamed |e author |
| 700 | 1 | 0 | |a Abderrahim, Belmadani |e author |
| 245 | 0 | 0 | |a A human vision based system for biometric images recognition |
| 260 | |b Institute of Advanced Engineering and Science, |c 2022-03-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27247 | ||
| 520 | |a In this paper, a universal biometric system based on human vision is proposed. From recent biological and physiological results, A human identification system that approximates the natural vision and recognition of individuals is conceived. Liquid state machine (LSM), as a recurrent spiking neural network, is highly inspired by the brain neural architecture with low training cost. However, input dimension of large scale images requires efficient processing at the cost of performance or resource overhead. This paper propose a new neural input coding for images based on frequency signals rather than pixels. Each image is filtered and fragmented then the LSM liquid (or reservoir) will receive, first, high frequency signals, then low frequency signals from each fragment. The two sets of output neurons states corresponding to each type of filter will be matched to the entire enrollment database. A weighted sum rule between the matching results will determine the right class of a biometric image. The system was tested on three different biometric datasets: face, palmprint and off-line signature, results show the reliability of the proposed approach. | ||
| 540 | |a Copyright (c) 2022 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Biometric; Frequency filtering; Human vision; Input coding; Liquid state machine | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 25, No 3: March 2022; 1508-1517 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v25.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27247/16097 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27247/16097 |z Get fulltext |