{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T16:35:29Z","timestamp":1778344529505,"version":"3.51.4"},"reference-count":0,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:p> Biometric identification technology has become increasingly common in our daily lives as the requirement for information protection and control legislation has grown around the world. The unimodal biometric systems use only biometric traits to authenticate the user which is trustworthy but it possesses various limitations such as susceptibility to attacks, noise occurring in a dataset, non-universality challenges, etc. Multimodal biometrics technology has the potential to avoid the various fundamental constraints of unimodal biometric systems and also it has garnered interest and popularity in this respect. In this research, an efficient multimodal biometric recognition system based on a deep learning approach is proposed. The structure is implemented around convolutional neural networks (CNN) in which feature extraction and Softmax classifier are used to identify images. This method employs three CNN models for iris, face, and fingerprint were integrated to create the system. The two levels of fusion strategy such as feature level fusion and score level fusion were employed. The efficiency of the proposed model is evaluated based on the two most popular multimodal datasets as SDUMLA-HMT and BiosecureID biometric dataset. The result analysis demonstrates that the proposed multimodal biometric recognition provides the enhanced result with higher accuracy of 99.92%, a lower equal error rate of 0.10% on feature level, and 0.08% on score level fusion. Similarly, the average FAR is 0.09% and the average FRR is 0.06%. Because of this enhanced result, the proposed approach is computationally efficient. <\/jats:p>","DOI":"10.1142\/s0218213023400171","type":"journal-article","created":{"date-parts":[[2023,2,6]],"date-time":"2023-02-06T02:12:50Z","timestamp":1675649570000},"source":"Crossref","is-referenced-by-count":22,"title":["Efficient Multimodal Biometric Recognition for Secure Authentication Based on Deep Learning Approach"],"prefix":"10.1142","volume":"32","author":[{"given":"Vani","family":"Rajasekar","sequence":"first","affiliation":[{"name":"Department of CSE, Kongu Engineering College, Perundurai, Erode, Thoppupalayam, Kumaran Nagar, Perundurai, Tamil Nadu 638060, India"}]},{"given":"Muzafer","family":"Saracevic","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, University of Novi Pazar, Dimitrija Tucovica bb, 36300 Novi Pazar, Serbia"}]},{"given":"Mahmoud","family":"Hassaballah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia"}]},{"given":"Darjan","family":"Karabasevic","sequence":"additional","affiliation":[{"name":"Faculty of Applied Management, Economics and Finance, University Business Academy in Novi Sad, Jevrejska 24\/1, 11000 Belgrade, Serbia"}]},{"given":"Dragisa","family":"Stanujkic","sequence":"additional","affiliation":[{"name":"Technical Faculty in Bor, University of Belgrade, Vojske Jugoslavije 12, 19210 Bor, Serbia"}]},{"given":"Mahir","family":"Zajmovic","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, University \u201cVITEZ\u201d, \u0160kolska 23, 72270, Travni, Bosnia and Herzegovina"}]},{"given":"Usman","family":"Tariq","sequence":"additional","affiliation":[{"name":"Prince Sattam bin Abdul Aziz University, Al-Kharj 16278, Saudi Arabia"}]},{"given":"Premalatha","family":"Jayapaul","sequence":"additional","affiliation":[{"name":"Department of IT, Kongu Engineering College, Perundurai, Erode, Thoppupalayam, Kumaran Nagar, Perundurai, Tamil Nadu 638060, India"}]}],"member":"219","published-online":{"date-parts":[[2023,5,22]]},"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213023400171","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T08:45:36Z","timestamp":1684917936000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S0218213023400171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5]]},"references-count":0,"journal-issue":{"issue":"03","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["10.1142\/S0218213023400171"],"URL":"https:\/\/doi.org\/10.1142\/s0218213023400171","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5]]},"article-number":"2340017"}}