Abstract
Biometric systems are widely used in various applications of today’s authentication technology. The unimodal systems suffer from various stumbling blocks such as noisy inputs, non-universality, intra-class variability and imposter spoofing which affects the system performance and accuracy. To effectively handle these problems, two or more individual modalities are used. In this paper, we presented a multimodal approach for fingerprint verification based on a combination of score level fusion rules. In the preprocessing stage, Anisotropic Diffusion Filter (ADF) and Histogram Equalization (Hist-Eq) techniques were applied to overcome the main challenging drawbacks of fingerprint samples acquisition such as distortion, noise, rotation, etc. Supplementary, the Local Binary Pattern (LBP) was used for feature extraction. In score level fusion, the matching scores of individual fingerprints were combined via several fusion rules. Receiver Operating Characteristics (ROC) curves were formed for the multimodal approach that’s why it is mainly used to evaluate our system. Experimental results shown improvements of the multimodal system using ADF and Hist-Eq versus the unimodal non-preprocessed fingerprint samples. The obtained results indicated that there is a significant increase in the performance of the proposed system due to the combination of scores, making it suitable for more applications relevant to identity verification.
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Ahmed, M.A.O., Reyad, O., AbdelSatar, Y., Omran, N.F. (2018). Multi-filter Score-Level Fusion for Fingerprint Verification. In: Hassanien, A., Tolba, M., Elhoseny, M., Mostafa, M. (eds) The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018). AMLTA 2018. Advances in Intelligent Systems and Computing, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-74690-6_61
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DOI: https://doi.org/10.1007/978-3-319-74690-6_61
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