{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T12:32:04Z","timestamp":1780317124005,"version":"3.54.1"},"reference-count":30,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>Despite significant advancements in fingerprint-based authentication, existing models still suffer from challenges such as high false acceptance and rejection rates, computational inefficiency, and vulnerability to spoofing attacks. Addressing these limitations is crucial for ensuring reliable biometric security in real-world applications, including law enforcement, financial transactions, and border security. This study proposes a hybrid deep learning approach that integrates Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) networks to enhance fingerprint authentication accuracy and robustness. The CNN component efficiently extracts intricate fingerprint patterns, while the LSTM module captures sequential dependencies to refine feature representation. The proposed model achieves a classification accuracy of 99.42%, reducing the false acceptance rate (FAR) to 0.31% and the false rejection rate (FRR) to 0.27%, demonstrating a 12% improvement over traditional CNN-based models. Additionally, the optimized architecture reduces computational overheads, ensuring faster processing suitable for real-time authentication systems. These findings highlight the superiority of hybrid deep learning techniques in biometric security by providing a quantifiable enhancement in both accuracy and efficiency. This research contributes to the advancement of secure, adaptive, and high-performance fingerprint authentication systems, bridging the gap between theoretical advancements and real-world applications.<\/jats:p>","DOI":"10.3390\/computers14050178","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T05:54:43Z","timestamp":1746510883000},"page":"178","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Hybrid Deep Learning Approach for Secure Biometric Authentication Using Fingerprint Data"],"prefix":"10.3390","volume":"14","author":[{"given":"Abdulrahman","family":"Hussian","sequence":"first","affiliation":[{"name":"Department of Computer Science, Faculty of Computer and Information Technology, Sana\u2019a University, Sanaa 37444, Yemen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Foud","family":"Murshed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computer and Information Technology, Sana\u2019a University, Sanaa 37444, Yemen"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6491-9938","authenticated-orcid":false,"given":"Mohammed Nasser","family":"Alandoli","sequence":"additional","affiliation":[{"name":"Faculty of Computing, Multimedia University, Cyberjaya 63100, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghalib","family":"Aljafari","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computer and Information Technology, Sana\u2019a University, Sanaa 37444, Yemen"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,6]]},"reference":[{"key":"ref_1","first-page":"45","article-title":"Advances in Biometric Authentication Systems: A Survey","volume":"8","author":"Johnson","year":"2024","journal-title":"J. Biom. Secur."},{"key":"ref_2","first-page":"85","article-title":"Hybrid Models in Biometric Systems: A Comparative Study","volume":"36","author":"Chen","year":"2024","journal-title":"Int. J. Mach. Learn. Appl."},{"key":"ref_3","unstructured":"Lee, D., and Ahmed, S. (2024, January 10\u201311). Enhancing Biometric Security with CNN-LSTM Architectures. Proceedings of the 2024 International Conference on Deep Learning and Applications, Dijon, France."},{"key":"ref_4","first-page":"45","article-title":"Scalability in Fingerprint Recognition Systems: Challenges and Opportunities","volume":"32","author":"Hernez","year":"2024","journal-title":"Biom. Technol. Today"},{"key":"ref_5","first-page":"110","article-title":"Biometric Security in Critical Applications: A Review","volume":"18","author":"Miller","year":"2024","journal-title":"J. Adv. Secur. Stud."},{"key":"ref_6","first-page":"33000","article-title":"Future Directions in Fingerprint Recognition Technology","volume":"52","author":"Zhao","year":"2024","journal-title":"IEEE Access"},{"key":"ref_7","first-page":"12","article-title":"Emerging Trends in Biometric Authentication","volume":"42","author":"Chen","year":"2024","journal-title":"J. Biom. Adv."},{"key":"ref_8","first-page":"54","article-title":"Advances in Biometric Systems Using Deep Learning","volume":"35","author":"Kumar","year":"2023","journal-title":"Int. J. Mach. Learn. Appl."},{"key":"ref_9","first-page":"8900","article-title":"Applications of Deep Learning in Biometrics","volume":"62","author":"Lee","year":"2024","journal-title":"IEEE Access"},{"key":"ref_10","first-page":"110","article-title":"LeNet-5-Based Fingerprint Recognition","volume":"15","author":"Radzi","year":"2024","journal-title":"J. Biom. Res."},{"key":"ref_11","first-page":"45","article-title":"CNN Architectures for Biometric Systems","volume":"103","author":"Das","year":"2023","journal-title":"Pattern Recognit. Lett."},{"key":"ref_12","first-page":"80","article-title":"Transfer Learning in Fingerprint Authentication","volume":"10","author":"Fairuz","year":"2023","journal-title":"Mach. Vis. J."},{"key":"ref_13","first-page":"95","article-title":"CenterRanked Loss for Fingerprint Recognition","volume":"63","author":"Su","year":"2024","journal-title":"Pattern Recognit. Adv."},{"key":"ref_14","first-page":"25","article-title":"Temporal Feature Analysis in Biometrics","volume":"41","author":"Mohammed","year":"2024","journal-title":"Deep. Learn. Appl. J."},{"key":"ref_15","first-page":"305","article-title":"LSTM in Fingerprint Recognition: A Review","volume":"30","author":"Yang","year":"2023","journal-title":"J. Biom. Syst."},{"key":"ref_16","first-page":"11150","article-title":"Hybrid CNN-LSTM Architectures for Biometric Systems","volume":"55","author":"Jang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_17","first-page":"80","article-title":"Using ResNet50 and LSTM for Fingerprint Recognition","volume":"37","author":"Minaee","year":"2023","journal-title":"J. Mach. Learn. Appl. Biom."},{"key":"ref_18","first-page":"99","article-title":"Tackling Spoofing and Variability in Fingerprint Recognition: New Approaches and Challenges","volume":"12","author":"Lee","year":"2024","journal-title":"Biom. Technol. J."},{"key":"ref_19","first-page":"125","article-title":"Enhancing Fingerprint Recognition Using Feedforward Neural Networks","volume":"12","author":"Khetri","year":"2023","journal-title":"Biom. Appl. J."},{"key":"ref_20","first-page":"90","article-title":"ANN and RNN Variants for Low-Quality Fingerprint Recognition","volume":"45","author":"Yang","year":"2024","journal-title":"J. Pattern Recognit."},{"key":"ref_21","first-page":"67","article-title":"Optimization Techniques for Biometric Recognition","volume":"40","author":"Gomez","year":"2024","journal-title":"Mach. Learn. Adv. Biom."},{"key":"ref_22","first-page":"2345","article-title":"Hybrid CNN-LSTM Networks for Fingerprint Recognition","volume":"18","author":"Liu","year":"2023","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_23","first-page":"11234","article-title":"Deep Learning Approaches for Fingerprint Liveness Detection","volume":"12","author":"Zhang","year":"2024","journal-title":"IEEE Access"},{"key":"ref_24","first-page":"100","article-title":"CNN-LSTM Ensembles in Fingerprint Recognition","volume":"19","author":"Wang","year":"2024","journal-title":"ACM Trans. Biom."},{"key":"ref_25","first-page":"200","article-title":"Deep Learning Techniques for Secure Fingerprint Recognition","volume":"12","author":"Johnson","year":"2024","journal-title":"IEEE Trans. Biom. Identity Sci."},{"key":"ref_26","first-page":"101","article-title":"Fingerprint Recognition Using Convolutional Neural Networks: A Review","volume":"15","author":"Chen","year":"2024","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","first-page":"109123","article-title":"Multimodal Deep Learning for Fingerprint Spoof Detection","volume":"135","author":"Khan","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_28","unstructured":"Shehu, Y.I., Ruiz-Garcia, A., Palade, V., and James, A. (2024, July 01). Sokoto Coventry Fingerprint Dataset (SOCOFing). Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/ruizgara\/socofing."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1109\/34.990140","article-title":"FVC2000: Fingerprint Verification Competition","volume":"24","author":"Maio","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","unstructured":"Wyzykowski, A.B.V., Segundo, M.P., and Lemes, R.P. (2020). Level Three Synthetic Fingerprint Generation. arXiv."}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/5\/178\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:27:36Z","timestamp":1760030856000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/5\/178"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,6]]},"references-count":30,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["computers14050178"],"URL":"https:\/\/doi.org\/10.3390\/computers14050178","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,6]]}}}