Fake Fingerprint Classification Using Hybrid Features Learning With Gradient Boosting
Biometric security systems must be able to detect phony fingerprints to provide reliable authentication. The findings of this study suggest a hybrid approach to the detection of fake fingerprints that uses information on the texture and shape of the fingerprint. The novelty of this approach lies in...
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| Veröffentlicht in: | Applied Computational Intelligence and Soft Computing Jg. 2025; H. 1 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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New York
John Wiley & Sons, Inc
01.01.2025
Wiley |
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| ISSN: | 1687-9724, 1687-9732 |
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| Abstract | Biometric security systems must be able to detect phony fingerprints to provide reliable authentication. The findings of this study suggest a hybrid approach to the detection of fake fingerprints that uses information on the texture and shape of the fingerprint. The novelty of this approach lies in integrating both traditional fingerprint information and geometric features obtained through wavelet transformation, which has not been extensively explored in previous studies. The proposed procedure uses the traditional fingerprint information and the geometric features that may be collected by wavelet modification. This allows it to take advantage of the complementary capabilities that these two types of capabilities offer. In addition, the hybrid feature set improves the system’s robustness and accuracy by leveraging each feature type’s unique strengths. To achieve this goal, the standard fingerprint information and the geometric aspects of the fingerprint are combined. It is possible to efficiently identify authentic and forged fingerprints by using these hybrid features and training a gradient boosting classifier. The findings of the studies demonstrate that the suggested technique achieves an accuracy of 96% on medium spoofing photos from the SOCOFing dataset, 97% on hard spoofing images, and 98% on mixed spoofing images. This high level of accuracy, especially on mixed spoofing images, showcases the effectiveness of the novel hybrid approach in diverse and challenging scenarios. This places it in the position of being the most accurate way currently accessible among the existing state‐of‐the‐art methods. Furthermore, the proposed method’s scalability and adaptability make it suitable for real‐world applications, potentially setting a new standard in biometric security. There is a great deal of optimism that the technique that has been described can increase the reliability and safety of biometric systems when used in situations representative of the actual world. |
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| AbstractList | Biometric security systems must be able to detect phony fingerprints to provide reliable authentication. The findings of this study suggest a hybrid approach to the detection of fake fingerprints that uses information on the texture and shape of the fingerprint. The novelty of this approach lies in integrating both traditional fingerprint information and geometric features obtained through wavelet transformation, which has not been extensively explored in previous studies. The proposed procedure uses the traditional fingerprint information and the geometric features that may be collected by wavelet modification. This allows it to take advantage of the complementary capabilities that these two types of capabilities offer. In addition, the hybrid feature set improves the system’s robustness and accuracy by leveraging each feature type’s unique strengths. To achieve this goal, the standard fingerprint information and the geometric aspects of the fingerprint are combined. It is possible to efficiently identify authentic and forged fingerprints by using these hybrid features and training a gradient boosting classifier. The findings of the studies demonstrate that the suggested technique achieves an accuracy of 96% on medium spoofing photos from the SOCOFing dataset, 97% on hard spoofing images, and 98% on mixed spoofing images. This high level of accuracy, especially on mixed spoofing images, showcases the effectiveness of the novel hybrid approach in diverse and challenging scenarios. This places it in the position of being the most accurate way currently accessible among the existing state‐of‐the‐art methods. Furthermore, the proposed method’s scalability and adaptability make it suitable for real‐world applications, potentially setting a new standard in biometric security. There is a great deal of optimism that the technique that has been described can increase the reliability and safety of biometric systems when used in situations representative of the actual world. |
| Audience | Academic |
| Author | Rashid, Javed Ali, Shujaat Tahir, Muhammad Ali, Muhammad Salman Akram, Arslan Jaffar, Muhammad Arfan Shah, Dilawar |
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| Copyright | COPYRIGHT 2025 John Wiley & Sons, Inc. Copyright © 2025 Muhammad Salman Ali et al. Applied Computational Intelligence and Soft Computing published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License (the “License”), which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| References | e_1_2_10_22_2 e_1_2_10_44_2 e_1_2_10_20_2 e_1_2_10_43_2 e_1_2_10_21_2 e_1_2_10_41_2 e_1_2_10_40_2 Ali A. M. (e_1_2_10_1_2) 2024; 14 e_1_2_10_19_2 Ahmed M. (e_1_2_10_32_2) 2023; 4 B D. (e_1_2_10_38_2) 2020 Ross A. A. (e_1_2_10_7_2) 2006 e_1_2_10_17_2 Ahmad S. (e_1_2_10_16_2) 2022 e_1_2_10_18_2 e_1_2_10_39_2 e_1_2_10_5_2 e_1_2_10_15_2 e_1_2_10_4_2 e_1_2_10_37_2 e_1_2_10_13_2 e_1_2_10_36_2 e_1_2_10_6_2 e_1_2_10_14_2 e_1_2_10_35_2 e_1_2_10_9_2 e_1_2_10_11_2 e_1_2_10_34_2 e_1_2_10_8_2 e_1_2_10_12_2 e_1_2_10_33_2 e_1_2_10_10_2 Kim J. (e_1_2_10_23_2) 2020; 63 e_1_2_10_31_2 Akram A. (e_1_2_10_42_2) 2023; 3 Chougule A. (e_1_2_10_26_2) 2019 Mathur S. (e_1_2_10_2_2) 2016 Nahar P. (e_1_2_10_28_2) 2018; 5 Ametefe D. S. (e_1_2_10_29_2) 2023; 39 Das S. (e_1_2_10_3_2) 2011; 1 e_1_2_10_27_2 Li X. (e_1_2_10_30_2) 2020; 8 e_1_2_10_24_2 e_1_2_10_25_2 |
| References_xml | – ident: e_1_2_10_33_2 doi: 10.3390/sym13050750 – ident: e_1_2_10_36_2 doi: 10.1109/ACCESS.2020.2990909 – volume: 8 year: 2020 ident: e_1_2_10_30_2 article-title: Fingerprint Liveness Detection Based on Fine-Grained Feature Fusion for Intelligent Devices publication-title: Mathematics – ident: e_1_2_10_18_2 doi: 10.1007/s11042-020-09314-6 – volume: 39 start-page: 1703 year: 2023 ident: e_1_2_10_29_2 article-title: Fingerprint Pattern Classification Using Deep Transfer Learning and Data Augmentation publication-title: The Visual Computer – ident: e_1_2_10_15_2 doi: 10.1007/s11042-023-16776-x – ident: e_1_2_10_43_2 doi: 10.56536/jicet.v3i1.55 – ident: e_1_2_10_39_2 doi: 10.1016/j.net.2020.03.022 – ident: e_1_2_10_21_2 doi: 10.1109/ICMLA.2018.00187 – ident: e_1_2_10_19_2 doi: 10.1109/TPAMI.2011.161 – ident: e_1_2_10_22_2 – ident: e_1_2_10_31_2 doi: 10.1007/s00521-019-04499-w – ident: e_1_2_10_44_2 doi: 10.3390/jimaging9080158 – ident: e_1_2_10_5_2 doi: 10.1145/982507.982516 – start-page: 1 year: 2022 ident: e_1_2_10_16_2 article-title: Fingerprint Classification Using Deep Learning publication-title: 2022 First International Conference on Artificial Intelligence Trends and Pattern Recognition (ICAITPR) – volume: 3 year: 2023 ident: e_1_2_10_42_2 article-title: Recognizing Facial Expressions Across Cultures Using Gradient Features publication-title: Journal of Innovative Computing and Emerging Technologies – volume: 14 year: 2024 ident: e_1_2_10_1_2 article-title: A Novel Multi-Biometric Technique for Verification of Secure E-Document publication-title: International Journal of Electrical and Computer Engineering – ident: e_1_2_10_14_2 doi: 10.32604/cmc.2023.035287 – ident: e_1_2_10_10_2 doi: 10.1007/978-0-85729-748-8 – ident: e_1_2_10_40_2 doi: 10.1007/978-3-642-04070-2_21 – ident: e_1_2_10_8_2 doi: 10.32604/cmc.2023.032005 – year: 2020 ident: e_1_2_10_38_2 article-title: Extracting Regions of Interest From Images publication-title: The Medium – ident: e_1_2_10_4_2 doi: 10.1145/2933241 – ident: e_1_2_10_11_2 doi: 10.1007/s11760-022-02270-8 – start-page: 1 year: 2016 ident: e_1_2_10_2_2 article-title: Methodology for Partial Fingerprint Enrollment and Authentication on Mobile Devices publication-title: International Conference on Biometrics (ICB) – ident: e_1_2_10_35_2 doi: 10.1109/ACCESS.2020.3047723 – ident: e_1_2_10_41_2 doi: 10.32604/cmc.2023.041074 – ident: e_1_2_10_12_2 doi: 10.1016/j.patcog.2022.109050 – volume: 63 year: 2020 ident: e_1_2_10_23_2 article-title: Left or Right Hand Classification From Fingerprint Images Using a Deep Neural Network publication-title: Computers, Materials and Continua – start-page: 1084 year: 2019 ident: e_1_2_10_26_2 article-title: Local Binary Pattern With Hyperparameter Tuned Support Vector Machine for Fingerprint Classification publication-title: 2019 International Conference on Intelligent Computing and Control Systems (ICCS) – ident: e_1_2_10_37_2 doi: 10.14500/aro.10975 – ident: e_1_2_10_13_2 doi: 10.1108/WJE-09-2020-0456 – ident: e_1_2_10_27_2 doi: 10.1109/ASPCON49795.2020.9276660 – volume: 4 start-page: 41 year: 2023 ident: e_1_2_10_32_2 article-title: A Deep Learning Approach in Detailed Fingerprint Identification publication-title: Computer Vision and Image Analysis for Industry – ident: e_1_2_10_17_2 doi: 10.1007/978-3-031-14054-9_31 – ident: e_1_2_10_6_2 doi: 10.1007/978-3-030-83624-5 – ident: e_1_2_10_24_2 doi: 10.1007/s00371-021-02173-8 – volume: 5 start-page: 1521 year: 2018 ident: e_1_2_10_28_2 article-title: Fingerprint Classification Using Deep Neural Network Model Resnet50 publication-title: International Journal of Research and Analytical Reviews – volume: 1 year: 2011 ident: e_1_2_10_3_2 article-title: Designing a Biometric Strategy (Fingerprint) Measure for Enhancing ATM Security in Indian E-Banking System publication-title: International Journal of Information and Communication Technology Research – ident: e_1_2_10_20_2 doi: 10.21123/bsj.2022.6550 – ident: e_1_2_10_9_2 doi: 10.3390/app12115714 – volume-title: Handbook of Multibiometrics year: 2006 ident: e_1_2_10_7_2 – ident: e_1_2_10_25_2 doi: 10.5220/0011327100003271 – ident: e_1_2_10_34_2 doi: 10.1109/TCYB.2021.3081764 |
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| SubjectTerms | Access control Accuracy Biometric identification Biometrics Biometry Classification Cooperation Deep learning Fingerprints Identification systems Information systems Literature reviews Safety and security measures Security systems Spoofing Wavelet transforms |
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| Title | Fake Fingerprint Classification Using Hybrid Features Learning With Gradient Boosting |
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