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Multimodal Biometric System Using Ear and Palm Vein Recognition Based on GwPeSOA: Multi-SVNN for Security Applications

In: New Trends in Computational Vision and Bio-inspired Computing

Author

Listed:
  • M. Vijay

    (PSY Engineering College, Department of ECE)

  • G. Indumathi

    (Mepco Schlenk Engineering College, Department of ECE)

Abstract

The human recognition is achieved easier and cheaper and the single modality employed for the recognition faces a lot of challenges due to the environmental factors. This paper proposes a multimodal recognition system based on the Multi-Support Vector Neural Network (Multi-SVNN). The algorithm proposed is Glowworm Penguin Search Optimization Algorithm (GwPeSOA), which is the modification of the Glowworm Optimization Algorithm (GOA) with the Penguin Search Optimization Algorithm (PeSOA). The proposed method employs ear and the palm vein modalities and the features of the ear image is obtained using the proposed BiComp masking method of feature extraction, whereas the features from the palm vein is extracted using the Local Binary Pattern method. The features obtained are applied to the Multi-SVNN classifier to recognize with good accuracy and the proposed BiComp Mask offers the robust features for the extraction. The experimentation using the proposed method attained a better accuracy, specificity, and sensitivity.

Suggested Citation

  • M. Vijay & G. Indumathi, 2020. "Multimodal Biometric System Using Ear and Palm Vein Recognition Based on GwPeSOA: Multi-SVNN for Security Applications," Springer Books, in: S. Smys & Abdullah M. Iliyasu & Robert Bestak & Fuqian Shi (ed.), New Trends in Computational Vision and Bio-inspired Computing, pages 215-231, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-41862-5_20
    DOI: 10.1007/978-3-030-41862-5_20
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