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Multimodal biometric identification based on overlapped fingerprints, palm prints, and finger knuckles using BM-KMA and CS-RBFNN techniques in forensic applications

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Abstract

In several scenarios like forensic and civilian applications, biometric has emerged as a powerful technology for person authentication. Information extracted from different biometric traits is combined by the Multimodal Biometric (MB) solutions, hence showing a high resilience against presentation attacks. Additionally, they offer enhanced biometric performance and increased population coverage that is required for executing larger-scale recognition. By employing Brownian Motion enabled K-Means Algorithm (BM-KMA) and Cosine Swish activation-based Radial Basis Function Neural Network (RBFNN) (CS-RBFNN) methodologies, an MB authentication system centered on overlapped Fingerprints (FPs), Palm Prints (PPs), and finger knuckles (FKs) is proposed here. Primarily, from the publically available datasets, the overlapped FP images and hand images are taken. Next, to separate the PPs and FKs, the Region of Interest (ROI) is estimated for the hand image. Then, pre-processing, feature extraction, and feature reduction are carried out. From the overlapped FP, the noises are removed using BF; after that, the FP’s contrast is enriched using SMF-CLAHE for improving the clarity of the minutiae structure of the ridges. Following this, normalization is performed using the Min–Max operation. Minute features are extracted by separating the overlapped FP using BM-KMA, which makes the system from avoidance of system complexity by separating the overlapping. From this, interest features are selected using KRC-PCA. Next, feature fusion is conducted. Finally, CS-RBFNN is wielded to categorize genuine biometrics from imposter ones. Via performance metrics, the proposed system is further affirmed. The outcomes exhibited that the proposed technique surpasses the other prevailing methodologies.

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Acknowledgements

We thank the anonymous referees for their useful suggestions.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumaracoil, Thuckalay, Kanyakumari, Tamil Nadu, India

    Jyothi Johnson

  2. Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India

    R. Chitra

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  1. Jyothi Johnson
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by JJ and RC. The first draft of the manuscript was written by JJS and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jyothi Johnson.

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Johnson, J., Chitra, R. Multimodal biometric identification based on overlapped fingerprints, palm prints, and finger knuckles using BM-KMA and CS-RBFNN techniques in forensic applications. Vis Comput 40, 3217–3231 (2024). https://doi.org/10.1007/s00371-023-03023-5

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