VHDL modeling of IRIS recognition using neural network
The application of neural networks technology to real-time processing of biometric identification demands the development of a new processing structure that allows efficient hardware implementation of the neural networks mechanism. This paper describes a VHDL modeling environment of IRIS recogn...
| Main Authors: | , , , , , |
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| Format: | Proceeding Paper |
| Language: | English |
| Published: |
2004
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/36657/ http://irep.iium.edu.my/36657/1/c-1_B63.pdf |
| _version_ | 1848781267849445376 |
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| author | Reaz, Mamun Bin Ibne T., A. Leng Mohd-Yasin, Faisal Sulaiman, M. S. Islam, S. Ibrahimy, Muhammad Ibn |
| author_facet | Reaz, Mamun Bin Ibne T., A. Leng Mohd-Yasin, Faisal Sulaiman, M. S. Islam, S. Ibrahimy, Muhammad Ibn |
| author_sort | Reaz, Mamun Bin Ibne |
| building | IIUM Repository |
| collection | Online Access |
| description | The application of neural networks technology to real-time
processing of biometric identification demands the
development of a new processing structure that allows
efficient hardware implementation of the neural networks
mechanism. This paper describes a VHDL modeling
environment of IRIS recognition for biometric identification
using neural network to ease the description, verification,
simulation and hardware realization of this kind of systems.
Iris has unique features to be used as a biometric signature
due to its speed, simplicity, accuracy, and applicability. The
processes of the project consist of two main parts, which are
image processing and recognition. Image processing done
by using Matlab where back propagation was used for
recognition. The iris recognition neural network
architecture comprises three layers: input layer with three
neurons, hidden layer with two neurons and output layer
with one neuron. Sigmoid transfer function is used for both
hidden layer and output layer neurons. Neuron of each layer
is modeled individually using behavioral VHDL. The layers
are then connected using structural VHDL. This is followed
by the timing analysis for the validation, functionality and
performance of the designated model. Iris vector from
captured human iris has been used to validate the
effectiveness of the model. Test on the sample of 100 data
showed an accuracy of 88.6% in recognizing the sample of
irises. |
| first_indexed | 2025-11-14T15:46:51Z |
| format | Proceeding Paper |
| id | iium-36657 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:46:51Z |
| publishDate | 2004 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-366572014-05-21T04:13:49Z http://irep.iium.edu.my/36657/ VHDL modeling of IRIS recognition using neural network Reaz, Mamun Bin Ibne T., A. Leng Mohd-Yasin, Faisal Sulaiman, M. S. Islam, S. Ibrahimy, Muhammad Ibn T Technology (General) The application of neural networks technology to real-time processing of biometric identification demands the development of a new processing structure that allows efficient hardware implementation of the neural networks mechanism. This paper describes a VHDL modeling environment of IRIS recognition for biometric identification using neural network to ease the description, verification, simulation and hardware realization of this kind of systems. Iris has unique features to be used as a biometric signature due to its speed, simplicity, accuracy, and applicability. The processes of the project consist of two main parts, which are image processing and recognition. Image processing done by using Matlab where back propagation was used for recognition. The iris recognition neural network architecture comprises three layers: input layer with three neurons, hidden layer with two neurons and output layer with one neuron. Sigmoid transfer function is used for both hidden layer and output layer neurons. Neuron of each layer is modeled individually using behavioral VHDL. The layers are then connected using structural VHDL. This is followed by the timing analysis for the validation, functionality and performance of the designated model. Iris vector from captured human iris has been used to validate the effectiveness of the model. Test on the sample of 100 data showed an accuracy of 88.6% in recognizing the sample of irises. 2004 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/36657/1/c-1_B63.pdf Reaz, Mamun Bin Ibne and T., A. Leng and Mohd-Yasin, Faisal and Sulaiman, M. S. and Islam, S. and Ibrahimy, Muhammad Ibn (2004) VHDL modeling of IRIS recognition using neural network. In: Second International Conference on Artificial Intelligence in Engineering & Technology, 2004, 3-5 Aug. 2004, Kota Kinabalu, Sabah. http://sktm.ums.edu.my/icaiet2014/ |
| spellingShingle | T Technology (General) Reaz, Mamun Bin Ibne T., A. Leng Mohd-Yasin, Faisal Sulaiman, M. S. Islam, S. Ibrahimy, Muhammad Ibn VHDL modeling of IRIS recognition using neural network |
| title | VHDL modeling of IRIS recognition using neural network |
| title_full | VHDL modeling of IRIS recognition using neural network |
| title_fullStr | VHDL modeling of IRIS recognition using neural network |
| title_full_unstemmed | VHDL modeling of IRIS recognition using neural network |
| title_short | VHDL modeling of IRIS recognition using neural network |
| title_sort | vhdl modeling of iris recognition using neural network |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/36657/ http://irep.iium.edu.my/36657/ http://irep.iium.edu.my/36657/1/c-1_B63.pdf |