Fusion Net-3: Denoising-based secure biometric authentication using fingerprints

Abstract

Fingerprint-based authentication is a critical biometric approach for ensuring security and accuracy. Traditional methods often face challenges such as noise and suboptimal feature extraction. To address the challenges, Fusion Net-3, an extensive model, is proposed to improve the speed, precision, and security level of fingerprint-based authentication systems. Fusion Net-3 operates through two separate stages: enrollment and authentication. During the enrollment phase, advanced pre-processing of fingerprint images was performed, incorporating an enhanced bilateral filter optimized with the seagull optimization algorithm. After pre-processing, features were obtained using a two-phase method: Zernike moments for shape-based features and local binary patterns for texture-based features. This helped ensure that fingerprint features were considered comprehensive for representation. For feature selection optimization, the falcon-inspired jackal optimization algorithm was proposed, a hybrid method combining the strengths of the golden jackal optimization and falcon optimization algorithm. Then, the selected features were combined using a combination of the geometric mean and the Fisher score to facilitate classification for a balanced and novel representation. During authentication, fingerprints were processed using similar techniques for consistency. Each fingerprint was labeled as genuine or fraudulent with the aid of the Fusion Net-3 model, which leverages the combined strengths of convolutional neural networks, ResNet-50, and U-Net. The model achieved an accuracy of 98.956% and a mean squared error of 0.0234 when implemented on a Python platform. Overall, the Fusion Net-3 model demonstrated superior performance compared to existing methods, effectively enhancing authentication accuracy and security.

Keywords

Authentication / Bilateral Filtering / Enrollment / Falcon Optimization / Fusion Net-3 / Golden Jackal Optimization / Seagull Optimization

References

[1] Abolfathi M., Inturi S., Banaei-Kashani F., & Jafarian J.H. (2024). Toward enhancing web privacy on HTTPS traffic: A novel SuperLearner attack model and an efficient defense approach with adversarial examples. Computers and Security, 139, 103673.https://doi.org/10.1016/j.cose.2023.103673

[2] Abolfathi M., Shomorony I., Vahid A., & Jafarian J.H. (2022). A game-Theoretically Optimal Defense Paradigm Against Traffic Analysis Attacks using Multipath Routing and Deception. In:Proceedings of the 27th ACM Symposium on Access Control Models and Technologies, p67-78. https://doi.org/10.1145/3532105.3535015

[3] Adiga V.S., & Sivaswamy J. (2019). Fpd-m-net:Fingerprint image denoising and inpainting using m-net based convolutional neural networks. In: Inpainting and Denoising Challenges. Cham: Springer International Publishing, p51-61.https://doi.org/10.1007/978-3-030-25614-24

[4] Afshari H.H., Gadsden S.A., & Habibi S. (2017). Gaussian filters for parameter and state estimation: A general review of theory and recent trends. Signal Processing, 135, 218-238. https://doi.org/10.1016/j.sigpro.2017.01.001

[5] Akanfe O., Lawong D., & Rao H.R. (2024). Blockchain technology and privacy regulation: Reviewing frictions and synthesizing opportunities. International Journal of Information Management, 76, 102753. https://doi.org/10.1016/j.ijinfomgt.2024.102753

[6] Akter A., Nosheen N., Ahmed S., Hossain M., Yousuf M.A., Almoyad M.A.A., et al. (2024). Robust clinical applicable CNN and U-Net based algorithm for MRI classification and segmentation for brain tumor. Expert Systems with Applications, 238, 122347. https://doi.org/10.1016/j.eswa.2023.122347

[7] Algarni M.Η. (2024), Fingerprint sequencing: An authentication mechanism that integrates fingerprints and a knowledge-based methodology to promote security and usability. Engineering, Technology and Applied Science Research, 14(3), 14233-14239. https://doi.org/10.48084/etasr.7250

[8] Ali S.S., Baghel V.S., Ganapathi I.I., & Prakash S. (2020). Robust biometric authentication system with a secure user template. Image and Vision Computing, 104, 104004. https://doi.org/10.1016/j.imavis.2020.104004

[9] Arini F.Y., Sunat K., & Soomlek C. (2022). Golden jackal optimization with joint opposite selection: An enhanced nature-inspired optimization algorithm for solving optimization problems. IEEE Access, 10, 28800-128823. Available from: https://www.kaggle.com/datasets/ruizgara/socofing

[10] Balsiger F., Jungo A., Scheidegger O., Carlier P.G., Reyes M., & Marty B. (2020). Spatially regularized parametric map reconstruction for fast magnetic resonance fingerprinting. Medical Image Analysis, 64, 101741. https://doi.org/10.1016/j.media.2020.101741

[11] Banitaba F.S., Aygun S., & Najafi M.H. (2024). Late Breaking Results: Fortifying Neural Networks: Safeguarding Against Adversarial Attacks with Stochastic Computing. [arXiv Preprint].

[12] Chopra N., & Ansari M.M. (2022). Golden jackal optimization: A novel nature-inspired optimizer for engineering applications. Expert Systems with Applications, 198, 116924.

[13] Dhiman G., & Kumar V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169-196. https://doi.org/10.1016/j.knosys.2018.11.024.

[14] Ding B., Wang H., Chen P., Zhang Y., Guo Z., Feng J., et al. (2020). Surface and internal fingerprint reconstruction from optical coherence tomography through convolutional neural network. IEEE Transactions on Information Forensics and Security, 16, 685-700. https://doi.org/10.1109/TIFS.2020.3016829

[15] Ephin M., & Vasanthi N.A. (2013). A highly secure integrated biometrics authentication using finger-palmprint fusion. International Journal of Scientific and Engineering Research, 4(1).

[16] Galbally J., Beslay L., & Böstrom G. (2020). 3D-FLARE: A touchless full-3D fingerprint recognition system based on laser sensing. IEEE Access, 8, 145513-145534. https://doi.org/10.1109/ACCESS.2020.3014796

[17] Gao Z., Gao Y., Wang S., Li D., Xu Y. (2020). CRISLoc: Reconstructable CSI fingerprinting for indoor smartphone localization. IEEE Internet of Things Journal, 8(5), 3422-3437. https://doi.org/10.1109/JIOT.2020.3022573

[18] Gavaskar R.G., & Chaudhury K.N. (2018). Fast adaptive bilateral filtering. IEEE Transactions on Image Processing, 28(2), 779-790. https://doi.org/10.1109/TIP.2018.2871597

[19] Gupta R., Khari M., Gupta D., & Crespo R.G. (2020). Fingerprint image enhancement and reconstruction using the orientation and phase reconstruction. Information Sciences, 530, 201-218. https://doi.org/10.1016/j.ins.2020.01.031

[20] Husson L., Bodin T., Spada G., Choblet G., & Kreemer C. (2018). Bayesian surface reconstruction of geodetic uplift rates: Mapping the global fingerprint of Glacial Isostatic Adjustment. Journal of Geodynamics, 122, 25-40. https://doi.org/10.1016/j.jog.2018.10.002

[21] Jia H., Xing Z., & Song W. (2019). A new hybrid seagull optimization algorithm for feature selection. IEEE Access, 7, 49614-49631. https://doi.org/10.1109/ACCESS.2019.2909945

[22] Kareem S.W., & Okur M.C. (2021). Falcon optimization algorithm for bayesian network structure learning. Computer Science, 22, 553-569. https://doi.org/10.7494/csci.2021.22.4.3773

[23] Khodadoust J., Khodadoust A.M., Mirkamali S.S., & Ayat S. (2020). Fingerprint indexing for wrinkled fingertips immersed in liquids. Expert Systems with Applications, 146, 113153. https://doi.org/10.1016/j.eswa.2019.113153

[24] Koonce B., & Koonce B.E. (2021). Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization. Apress, New York, USA. https://doi.org/10.1007/978-1-4842-6168-2

[25] Lee S.H., Kim W.Y., & Seo D.H. (2022). Automatic self-reconstruction model for radio map in Wi-Fi fingerprinting. Expert Systems with Applications, 192, 116455. https://doi.org/10.1016/j.eswa.2021.116455

[26] Li J., Feng J., & Kuo C.C.J. (2018). Deep convolutional neural network for latent fingerprint enhancement. Signal Processing: Image Communication, 60, 52-63. https://doi.org/10.1016/j.image.2017.08.010

[27] Li Y., Xia Q., Lee C., Kim S., & Kim J. (2022). A robust and efficient fingerprint image restoration method based on a phase-field model. Pattern Recognition, 123, 108405. https://doi.org/10.1016/j.patcog.2021.108405

[28] Liang Y., & Liang W. (2023). ResWCAE: Biometric Pattern Image Denoising Using Residual Wavelet-Conditioned Autoencoder. [Preprint]. https://doi.org/10.1007/978-981-96-6603-4_17

[29] Lin C., & Kumar A. (2018). Contactless and partial 3D fingerprint recognition using multi-view deep representation. Pattern Recognition, 83, 314-327. https://doi.org/10.1016/j.patcog.2018.05.004

[30] Liu F., Kong Z., Liu H., Zhang W., & Shen L. (2022). Fingerprint presentation attack detection by channel-wise feature denoising. IEEE Transactions on Information Forensics and Security, 17, 2963-2976. https://doi.org/10.1109/TIFS.2022.3197058

[31] Liu F., Liu G., Zhao Q., & Shen L. (2020a). Robust and high-security fingerprint recognition system using optical coherence tomography. Neurocomputing, 402, 14-28. https://doi.org/10.1016/j.neucom.2020.03.102

[32] Liu F., Liu H., Zhang W., Liu G., & Shen L. (2021). One-class fingerprint presentation attack detection using auto-encoder network. IEEE Transactions on Image Processing, 30, 2394-2407. https://doi.org/10.1109/TIP.2021.3052341.

[33] Liu F., Shen C., Liu H., Liu G., Liu Y., Guo, Z., et al. (2020b). A flexible touch-based fingerprint acquisition device and a benchmark database using optical coherence tomography. IEEE Transactions on Instrumentation and Measurement, 69(9), 6518-6529. https://doi.org/10.1109/TIM.2020.2967513

[34] Mahum R., Irtaza A., Nawaz M., Nazir T., Masood M., Shaikh S., et al. (2023). A robust framework to generate surveillance video summaries using combination of zernike moments and r-transform and a deep neural network. Multimedia Tools and Applications, 82(9), 13811-13835. https://doi.org/10.1007/s11042-022-13773-4

[35] Narodytska N., & Kasiviswanathan S.P. (2017). Simple Black-Box Adversarial Attacks on Deep Neural Networks. In: CVPR Workshops. Vol. 2. Available from: https://openaccess.thecvf.com/content_cvpr_2017_workshops/w16/papers/kasiviswanathan_simple_black-box_adversarial_cvpr_2017_paper.pdf

[36] Nasri M., Kosa M., Chukoskie L., Moghaddam M., & Harteveld C. (2024). Exploring Eye Tracking to Detect Cognitive Load in Complex Virtual Reality 1 Training. In: 2024 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). IEEE, p51-54. https://doi.org/10.1109/ISMAR-Adjunct6495 1.2024.00022

[37] Paris S., Kornprobst P., Tumblin J., & Durand F. (2009). Bilateral filtering: Theory and applications. Foundations and Trends® in Computer Graphics and Vision, 4(1), 1-73. https://doi.org/10.1561/0600000020

[38] Praseetha V.M., Bayezeed S., & Vadivel S. (2019). Secure fingerprint authentication using deep learning and minutiae verification. Journal of Intelligent Systems, 29(1), 1379-1387. https://doi.org/10.1515/jisys-2018-0289

[39] Prybylo M., Haghighi S., Peddinti S. T., & Ghanavati S. (2024b). Evaluating privacy perceptions, experience, and behavior of software development teams. USENIX. Available from: https://www.usenix.org/conference/soups2024/presentation/prybylo

[40] Rahman M.M., Mishu T.I., & Bhuiyan M.A.A. (2022). Performance analysis of a parameterized minutiae-based approach for securing fingerprint templates in biometric authentication systems. Journal of Information Security and Applications, 67, 103209. https://doi.org/10.1016/j.jisa.2022.103209

[41] Santos S., Breaux T., Norton T., Haghighi S., Ghanavati S. (2024). Requirements Satisfiability with in-Context Learning. [arXiv Preprint]. https://doi.org/10.1109/RE59067.2024.00025

[42] Shadab S.A., Ansari M.A., Singh N., Verma A., Tripathi P., & Mehrotra R. (2022). Detection of Cancer from Histopathology Medical Image Data Using ML with CNN ResNet-50 Architecture. In: Computational Intelligence in Healthcare Applications. Academic Press, United States, p237-254. https://doi.org/10.1016/B978-0-323-99031-8.00007-7

[43] Shehu Y.I., Ruiz-Garcia A., Palade V., & James A. (2018). Sokoto Coventry Fingerprint Dataset. [arXiv Preprint]. https://doi.org/10.48550/arXiv.1807.10609

[44] Srinivasan D.S., Ravichandran S., Indrani T.S., & Karpagam G.R. (2023). Local Binary Pattern-Based Criminal Identification System. In: Sustainable Digital Technologies for Smart Cities. United States: CRC Press. p45-56. Available from: https://www.taylorfrancis.com/chapters/edit/10.1201/9781003307716-4/local-binary-pattern-based-criminal-identification-system-dhana-srinithi-srinivasan-soundarya-ravichandran-thamizhi-shanmugam-indrani-karpagam

[45] Stolk C.C., & Sbrizzi A. (2019). Understanding the combined effect of kk-space undersampling and transient states excitation in MR fingerprinting reconstructions. IEEE Transactions on Medical Imaging, 38(10), 2445-2455. https://doi.org/10.1109/TMI.2019.2900585

[46] Trivedi A.K., Thounaojam D.M., & Pal S. (2020). Non-invertible cancellable fingerprint template for fingerprint biometrics. Computers and Security, 90, 101690. https://doi.org/10.1016/j.cose.2019.101690

[47] Vogel R.M. (2022). The geometric mean? Communications in Statistics-Theory and Methods, 51(1), 82-94. https://doi.org/10.1080/03610926.2020.1743313

[48] Wang D., Ostenson J., & Smith D.S. (2020). snapMRF: GPU-accelerated magnetic resonance fingerprinting dictionary generation and matching using extended phase graphs. Magnetic Resonance Imaging, 66, 248-256. https://doi.org/10.1016/j.mri.2019.11.015

[49] Wang W., & Yang Y. (2024). A histogram equalization model for color image contrast enhancement. Signal, Image and Video Processing, 18(2), 1725-1732. https://doi.org/10.1007/s11760-023-02881-9

[50] Wong W.J., & Lai S.H. (2020). Multi-task CNN for restoring corrupted fingerprint images. Pattern Recognition, 101, 107203. https://doi.org/10.1016/j.patcog.2020.107203

[51] Xu Z., Ye H., Lyu M., He H., Zhong J., Mei Y. (2019). Rigid motion correction for magnetic resonance fingerprinting with sliding-window reconstruction and image registration. Magnetic Resonance Imaging, 57, 303-312. https://doi.org/10.1016/j.mri.2018.11.001

[52] Yin X., Zhu Y., & Hu J. (2019). 3D fingerprint recognition based on ridge-valley-guided 3D reconstruction and 3D topology polymer feature extraction. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(3), 1085-1091. https://doi.org/10.1109/TPAMI.2019.2949299

[53] Zhang L., Tan T., Gong Y., & Yang W. (2019). Fingerprint database reconstruction based on robust PCA for indoor localization. Sensors (Basel), 19(11), 2537.

[54] Zheng H., Gao M., Chen Z., Liu X.Y., & Feng X. (2019). An adaptive sampling scheme via approximate volume sampling for fingerprint-based indoor localization. IEEE Internet of Things Journal, 6(2), 2338-2353. https://doi.org/10.1109/jiot.2019.2906489