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Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

Priti Shivaji Sanjekar, Jayantrao B. Patil
Copyright: © 2019 |Volume: 9 |Issue: 4 |Pages: 14
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781522567219|DOI: 10.4018/IJCVIP.2019100101
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MLA

Sanjekar, Priti Shivaji, and Jayantrao B. Patil. "Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach." IJCVIP vol.9, no.4 2019: pp.1-14. http://doi.org/10.4018/IJCVIP.2019100101

APA

Sanjekar, P. S. & Patil, J. B. (2019). Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach. International Journal of Computer Vision and Image Processing (IJCVIP), 9(4), 1-14. http://doi.org/10.4018/IJCVIP.2019100101

Chicago

Sanjekar, Priti Shivaji, and Jayantrao B. Patil. "Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach," International Journal of Computer Vision and Image Processing (IJCVIP) 9, no.4: 1-14. http://doi.org/10.4018/IJCVIP.2019100101

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Abstract

Multimodal biometrics is the frontier to unimodal biometrics as it integrates the information obtained from multiple biometric sources at various fusion levels i.e. sensor level, feature extraction level, match score level, or decision level. In this article, fingerprint, palmprint, and iris are used for verification of an individual. The wavelet transformation is used to extract features from fingerprint, palmprint, and iris. Further the PCA is used for dimensionality reduction. The fusion of traits is employed at three levels: feature level; feature level combined with match score level; and feature level combined with decision level. The main objective of this research is to observe effect of combined fusion levels on verification of an individual. The performance of three cases of fusion is measured in terms of EER and represented with ROC. The experiments performed on 100 different subjects from publicly available databases demonstrate that combining feature level with match score level and feature level with decision level fusion both outperforms fusion at only a feature level.

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