AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification...
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| Format: | Article |
| Language: | English |
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Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
2021-10-01
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| Series: | Journal of Engineering Studies and Research |
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| Online Access: | https://jesr.ub.ro/index.php/1/article/view/276 |
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| _version_ | 1823858349211385856 |
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| author | LAWRENCE OMOTOSHO IBRAHIM OGUNDOYIN OLAJIDE ADEBAYO JOSHUA OYENIYI |
| author_facet | LAWRENCE OMOTOSHO IBRAHIM OGUNDOYIN OLAJIDE ADEBAYO JOSHUA OYENIYI |
| author_sort | LAWRENCE OMOTOSHO |
| collection | DOAJ |
| description |
Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.
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| format | Article |
| id | doaj-art-83afe87c3c1d4bd899f1b0b75b347e79 |
| institution | Kabale University |
| issn | 2068-7559 2344-4932 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Alma Mater Publishing House "Vasile Alecsandri" University of Bacau |
| record_format | Article |
| series | Journal of Engineering Studies and Research |
| spelling | doaj-art-83afe87c3c1d4bd899f1b0b75b347e792025-02-11T11:40:14ZengAlma Mater Publishing House "Vasile Alecsandri" University of BacauJournal of Engineering Studies and Research2068-75592344-49322021-10-0127210.29081/jesr.v27i2.276AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORKLAWRENCE OMOTOSHOIBRAHIM OGUNDOYINOLAJIDE ADEBAYOJOSHUA OYENIYI Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%. https://jesr.ub.ro/index.php/1/article/view/276multimodal, biometric system, convolutional neural network |
| spellingShingle | LAWRENCE OMOTOSHO IBRAHIM OGUNDOYIN OLAJIDE ADEBAYO JOSHUA OYENIYI AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK Journal of Engineering Studies and Research multimodal, biometric system, convolutional neural network |
| title | AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK |
| title_full | AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK |
| title_fullStr | AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK |
| title_full_unstemmed | AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK |
| title_short | AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK |
| title_sort | enhanced multimodal biometric system based on convolutional neural network |
| topic | multimodal, biometric system, convolutional neural network |
| url | https://jesr.ub.ro/index.php/1/article/view/276 |
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