{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T12:33:39Z","timestamp":1768912419163,"version":"3.49.0"},"reference-count":18,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T00:00:00Z","timestamp":1652227200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Grant Scheme (FRGS) of the Ministry of Higher Education Malaysia","award":["FRGS\/1\/2019\/ICT02\/MMU\/02\/14"],"award-info":[{"award-number":["FRGS\/1\/2019\/ICT02\/MMU\/02\/14"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The finger vein recognition system uses blood vessels inside the finger of an individual for identity verification. The public is in favor of a finger vein recognition system over conventional passwords or ID cards as the biometric technology is harder to forge, misplace, and share. In this study, the histogram of oriented gradients (HOG) features, which are robust against changes in illumination and position, are extracted from the finger vein for personal recognition. To further increase the amount of information that can be used for recognition, different instances of the finger vein, ranging from the index, middle, and ring finger are combined to form a multi-instance finger vein representation. This fusion approach is preferred since it can be performed without requiring additional sensors or feature extractors. To combine different instances of finger vein effectively, score level fusion is adopted to allow greater compatibility among the wide range of matches. Towards this end, two methods are proposed: Bayesian optimized support vector machine (SVM) score fusion (BSSF) and Bayesian optimized SVM based fusion (BSBF). The fusion results are incrementally improved by optimizing the hyperparameters of the HOG feature, SVM matcher, and the weighted sum of score level fusion using the Bayesian optimization approach. This is considered a kind of knowledge-based approach that takes into account the previous optimization attempts or trials to determine the next optimization trial, making it an efficient optimizer. By using stratified cross-validation in the training process, the proposed method is able to achieve the lowest EER of 0.48% and 0.22% for the SDUMLA-HMT dataset and UTFVP dataset, respectively.<\/jats:p>","DOI":"10.3390\/a15050161","type":"journal-article","created":{"date-parts":[[2022,5,11]],"date-time":"2022-05-11T10:19:46Z","timestamp":1652264386000},"page":"161","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition"],"prefix":"10.3390","volume":"15","author":[{"given":"Jackson Horlick","family":"Teng","sequence":"first","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5867-9517","authenticated-orcid":false,"given":"Thian Song","family":"Ong","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0901-3831","authenticated-orcid":false,"given":"Tee","family":"Connie","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0540-2872","authenticated-orcid":false,"given":"Kalaiarasi","family":"Sonai Muthu Anbananthen","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]},{"given":"Pa Pa","family":"Min","sequence":"additional","affiliation":[{"name":"Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,11]]},"reference":[{"key":"ref_1","unstructured":"Ross, A., and Jain, A.K. (2004, January 6\u201310). Multimodal Biometrics: An Overview. Proceedings of the Signal Processing Conference, Vienna, Austria."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dass, S.C., Nandakumar, K., and Jain, A.K. (2005). A Principled Approach to Score Level Fusion in Multimodal Biometric Systems. International Conference on Audio- and Video-Based Biometric Person Authentication, Springer.","DOI":"10.1007\/11527923_109"},{"key":"ref_3","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of Oriented Gradients For Human Detection. Proceedings of the 2005 IEEE Computer Society Conference On Computer Vision And Pattern Recognition (CVPR), San Diego, CA, USA."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"William, A., Ong, T.S., Lau, S.H., and Goh, M.K.O. (2015, January 19\u201321). Finger Vein Verification Using Local Histogram of Hybrid Texture Descriptors. Proceedings of the 2015 Ieee International Conference On Signal And Image Processing Applications (Icsipa), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICSIPA.2015.7412209"},{"key":"ref_5","first-page":"5723","article-title":"Score Level Fusion of Fingerprint and Finger Vein Recognition","volume":"7","author":"Cui","year":"2011","journal-title":"J. Comput. Inf. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1016\/j.patrec.2011.11.002","article-title":"Feature-Level Fusion Of Fingerprint And Finger-Vein For Personal Identification","volume":"33","author":"Yang","year":"2012","journal-title":"Pattern Recognit. Lett."},{"key":"ref_7","first-page":"50","article-title":"Dynamic Weighting For Effective Fusion of Fingerprint And Finger Vein","volume":"1","author":"Yang","year":"2012","journal-title":"Pica. Prog. Intell. Comput. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1016\/j.patcog.2009.11.018","article-title":"Performance evaluation of score level fusion in multimodal biometric systems","volume":"43","author":"He","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.asoc.2016.02.008","article-title":"Multimodal Fusion of The Finger Vein, Fingerprint and The Finger-Knuckle-Print Using Kernel Fisher Analysis","volume":"42","author":"Abrishambaf","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kim, W., Song, J.M., and Park, K.R. (2018). Multimodal Biometric Recognition Based on Convolutional Neural Network by the Fusion of Finger-Vein and Finger Shape Using Near-Infrared (NIR) Camera Sensor. Sensors, 18.","DOI":"10.3390\/s18072296"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nickfarjam, A.M., Najafabadi, A.P., and Ebrahimpour-Komleh, H. (2014, January 20\u201322). Efficient Parameter Tuning for Histogram of Oriented Gradients. Proceedings of the 2014 22nd Iranian Conference on Electrical Engineering (ICEE), Tehran, Iran.","DOI":"10.1109\/IranianCEE.2014.6999687"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"57","DOI":"10.2478\/s11772-008-0054-8","article-title":"Multimodal biometric authentication based on score level fusion using support vector machine","volume":"17","author":"Wang","year":"2009","journal-title":"Opto-Electron. Rev."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Agrawal, T. (2021). Hyperparameter Optimization in Machine Learning, Apress Berkeley.","DOI":"10.1007\/978-1-4842-6579-6"},{"key":"ref_14","unstructured":"Dewancker, I., Mccourt, M., and Clark, S. (2022, January 02). Bayesian Optimization Primer. Available online: https:\/\/app.sigopt.com\/static\/pdf\/SigOpt_Bayesian_Optimization_Primer.pdf."},{"key":"ref_15","first-page":"2456","article-title":"Algorithms For Hyper-Parameter Optimization","volume":"24","author":"Bergstra","year":"2011","journal-title":"Adv. Neural Inf. Processing Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yin, Y., Liu, L., and Sun, X. (2011, January 3\u20134). SDUMLA-HMT: A Multimodal Biometric Database. Proceedings of the Biometric Recognition, Beijing, China.","DOI":"10.1007\/978-3-642-25449-9_33"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ton, B.T., and Veldhuis, R.N.J. (2013, January 4\u20137). A high quality finger vascular pattern dataset collected using a custom designed capturing device. Proceedings of the 2013 International Conference on Biometrics (ICB), Madrid, Spain.","DOI":"10.1109\/ICB.2013.6612966"},{"key":"ref_18","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/5\/161\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:09:09Z","timestamp":1760137749000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/15\/5\/161"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,11]]},"references-count":18,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["a15050161"],"URL":"https:\/\/doi.org\/10.3390\/a15050161","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,11]]}}}