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An optimized deep learning based depthwise separable MobileNetV3 approach for automatic finger vein recognition system

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Abstract

Recently, finger vein recognition has become integral to biometric technology that uses vein patterns for highly secure personal recognition. Many existing studies reported accurate finger vein recognition systems using recent techniques. However, the existing techniques face high complexity in analyzing vein patterns and increase recognition errors. Hence, this article proposes a novel deep learning (DL) technique for automatically recognizing finger veins at low time complexity. Initially, image de-noising and contrast enhancement are performed in the preprocessing stage using Extended Frost Filtering (Ex-FF) and Entropy Enriched Histogram Equalization (En-HE) to enhance the image's visual quality. The preprocessed images are then fed into the Kernel Attentive Resnet152v2 (KAtt-ResNet152V2) technique to extract the vein features efficiently. The extracted vein features are then fed to the Hybridized Golden Archerfish Optimizer (HyG-ArfO) to select the optimal vein features to reduce the feature dimensionalities. Finally, the optimal features are fed into the Residual Pyramid based Depthwise Separable MobileNetV3 (PyDS-MV3) technique to accurately recognize the person's finger veins. The proposed work is implemented in MATLAB, and a publicly available finger vein dataset is utilized. The performance measures such as accuracy, precision, false discovery rate (FDR), and Mathew's correlation coefficient (MCC) are analyzed and compared with existing studies. The proposed method obtains an overall accuracy of 99.6%, precision of 99.42%, FDR of 0.005 and MCC of 0.994.

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Authors and Affiliations

  1. College of Computer Science and Information Technology, Swami Ramanand Tirth Marathwada University, Ambejogai Road, Latur, Maharashtra, 413531, India

    Sambhaji Vamanrao Deshmukh & Nitish Shankar Zulpe

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  1. Sambhaji Vamanrao Deshmukh
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  2. Nitish Shankar Zulpe
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Correspondence to Sambhaji Vamanrao Deshmukh.

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Deshmukh, S.V., Zulpe, N.S. An optimized deep learning based depthwise separable MobileNetV3 approach for automatic finger vein recognition system. Multimed Tools Appl 83, 64285–64313 (2024). https://doi.org/10.1007/s11042-023-18070-2

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