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Fusion of multimodal biometric authentication using gradient pyramid, PCA and DWT

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  • R. Devi
  • P. Sujatha

Abstract

Authentication and identification is the most challenging task in our daily life. Biometric system provides an automatic identification of an individual using his/her behavioural or physiological traits. In this work, multimodal biometric traits namely fingerprint and iris, have been used. These traits were pre-processed using Wiener filter and applying some morphological operations. The pre-processed biometric traits were segmented and fused using three algorithms namely discrete wavelet transform (DWT), principal component analysis (PCA) and gradient pyramid (GP). The fused biometric traits using GP provides a better result without losing the meaningful information. The feature extraction and classification were carried out using grey scale co-occurrence matrices (GLCM) and support vector machine (SVM). Authentication using fused biometric traits gives accuracy as 83.75, whereas the accuracy using fingerprint 73.75% and iris was 78.48%.

Suggested Citation

  • R. Devi & P. Sujatha, 2023. "Fusion of multimodal biometric authentication using gradient pyramid, PCA and DWT," International Journal of Intelligent Enterprise, Inderscience Enterprises Ltd, vol. 10(1), pages 73-98.
    • Handle: RePEc:ids:ijient:v:10:y:2023:i:1:p:73-98
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