Multimodal Biometric Fusion Strategies: A Comparative Review of Current Trends, Challenges, and Future Directions

Khalid Jajan; Wafaa Mustafa Abduallah

doi:10.65542/djei.v2i2.28

Authors

Khalid Jajan Department of Information Technology, Akre University for Applied Science, Akre, Kurdistan, Iraq https://orcid.org/0009-0002-2983-6672
Wafaa Mustafa Abduallah Department of Information Technology, Duhok Polytechnic University, Duhok, Kurdistan, Iraq https://orcid.org/0009-0007-5496-8745

DOI:

https://doi.org/10.65542/djei.v2i2.28

Keywords:

Multimodal biometric systems, biometric fusion, deep learning, identity verification, score-level fusion, feature-level fusion, biometric security

Abstract

This paper tends to serve as a concise review of the recent literature on multimodal biometrics recognition systems, in particular, on the fusion strategies that are utilized at each stage of biometric data processing. These are feature-level, score-level, decision-level, serial, and hybrid fusions. The survey underscores that the fusion of multiple biometric traits, including fingerprints, face, iris, palmprints, voice, signature, etc., is more effective in increasing system performance, reliability, and spoof resistance than unimodal techniques. Classifying the studies based on the fusion strategies that they followed, this paper reviews the techniques, performance indicators, datasets, and application of the reviewed studies. The score-level fusion method gave the best reported accuracy of 100%, and the serial fusion obtained lower accuracy owing to the limitation of adaptability and dataset dependency. The review discusses and describes common problems and future directions for research as well. The insights drawn are targeted towards possibly the more secure, efficient and versatile multimodal biometric systems as far as the real-world applications are concerned.

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