An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics
This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiment...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/24/9706 |
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| author | Syed Aqeel Haider Shahzad Ashraf Raja Masood Larik Nusrat Husain Hafiz Abdul Muqeet Usman Humayun Ashraf Yahya Zeeshan Ahmad Arfeen Muhammad Farhan Khan |
| author_facet | Syed Aqeel Haider Shahzad Ashraf Raja Masood Larik Nusrat Husain Hafiz Abdul Muqeet Usman Humayun Ashraf Yahya Zeeshan Ahmad Arfeen Muhammad Farhan Khan |
| author_sort | Syed Aqeel Haider |
| collection | DOAJ |
| description | This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems. |
| first_indexed | 2024-03-08T20:22:43Z |
| format | Article |
| id | doaj.art-0f4a96b273bb47e388fe22a4b414dda0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T20:22:43Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0f4a96b273bb47e388fe22a4b414dda02023-12-22T14:40:18ZengMDPI AGSensors1424-82202023-12-012324970610.3390/s23249706An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein BiometricsSyed Aqeel Haider0Shahzad Ashraf1Raja Masood Larik2Nusrat Husain3Hafiz Abdul Muqeet4Usman Humayun5Ashraf Yahya6Zeeshan Ahmad Arfeen7Muhammad Farhan Khan8Department of Computer & Information Systems Engineering, Faculty of Computer & Electrical Engineering, N.E.D. University of Engineering and Technology, Karachi 75270, PakistanDepartment of Electrical Engineering, NFC Institute of Engineering and Technology, Multan 60000, PakistanDepartment of Electrical Engineering, N.E.D University of Engineering and Technology, Karachi 75270, PakistanDepartment of Electronics & Power Engineering, Pakistan Navy Engineering College, National University of Sciences and Technology (NUST), Islamabad 44000, PakistanElectrical Engineering Technology Department, Punjab Tianjin University of Technology, Lahore 54770, PakistanDepartment of Computer Engineering, Faculty of Engineering, Bahauddin Zakariya University (BZU), Multan 60800, PakistanDepartment of Electronics & Power Engineering, Pakistan Navy Engineering College, National University of Sciences and Technology (NUST), Islamabad 44000, PakistanDepartment of Electrical Engineering, The Islamia University of Bahawalpur, Bahawalpur 63100, PakistanDepartment of Electronics & Power Engineering, Pakistan Navy Engineering College, National University of Sciences and Technology (NUST), Islamabad 44000, PakistanThis research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.https://www.mdpi.com/1424-8220/23/24/9706biometric modalitiesconvolutional neural networkFinger Texture biometricFinger Vein biometricFuzzy Inference SystemLinear Binary Pattern |
| spellingShingle | Syed Aqeel Haider Shahzad Ashraf Raja Masood Larik Nusrat Husain Hafiz Abdul Muqeet Usman Humayun Ashraf Yahya Zeeshan Ahmad Arfeen Muhammad Farhan Khan An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics Sensors biometric modalities convolutional neural network Finger Texture biometric Finger Vein biometric Fuzzy Inference System Linear Binary Pattern |
| title | An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics |
| title_full | An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics |
| title_fullStr | An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics |
| title_full_unstemmed | An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics |
| title_short | An Improved Multimodal Biometric Identification System Employing Score-Level Fuzzification of Finger Texture and Finger Vein Biometrics |
| title_sort | improved multimodal biometric identification system employing score level fuzzification of finger texture and finger vein biometrics |
| topic | biometric modalities convolutional neural network Finger Texture biometric Finger Vein biometric Fuzzy Inference System Linear Binary Pattern |
| url | https://www.mdpi.com/1424-8220/23/24/9706 |
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