Abstract
The earlier research clearly indicated that the bimodal authentication system has more efficiency than unimodal and multimodal. This is due to the reason for the best intact biometric traits of fingerprint and retina. There is a chance to additionally improve further performance of the proposed biometric trait combination by additional or alternate algorithms or methodologies. Therefore, in this research work, the multi-kernel support vector machine (MK-SVM), a machine learning classification approach, is proposed and is used for the implementation. In addition, a hybrid algorithm of fragment Jaya optimizer-based deep convolutional neural network (FJO-DCNN) approach is also used to improve the performance value for bimodal biometric authentication and classification. The recognition systems analyze both biometrics independently, and their conclusions are combined to determine whether to give or refuse access to the user in the end. According to the findings of the implementation, this work demonstrates more dependability than the cascaded biometric system.
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Data Availability
The dataset generated and analyzed during the current study are available from the corresponding author on reasonable request.
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Acknowledgements
The authors acknowledged the Sathyabama Institute of Science and Technology, Chennai, India; Rajalakshmi Engineering College, Chennai, India and SRM Institute of Science and Technology, Ramapuram, Chennai, India for supporting the research work by providing the facilities.
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This article is part of the topical collection “Advances in Computational Approaches for Image Processing, Wireless Networks, Cloud Applications and Network Security” guest edited by P. Raviraj, Maode Ma and Roopashree H R.
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Umasankari, N., Muthukumar, B. & Shanmuganathan, C. Performance Evaluation of Biometric Authentication Using Fragment Jaya Optimizer-Based Deep CNN with Multi-kernel SVM. SN COMPUT. SCI. 5, 337 (2024). https://doi.org/10.1007/s42979-024-02666-y
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DOI: https://doi.org/10.1007/s42979-024-02666-y