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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
El-Sayed, M.A., Khafagy, M.A.: An identification system using eye detection based on wavelets and neural networks. arXiv preprint arXiv:1401.5108 (2014)
Khfagy, M., AbdelSatar, Y., Reyad, O., Omran, N.: An integrated smoothing method for fingerprint recognition enhancement. In: International Conference on Advanced Intelligent Systems and Informatics, pp. 407–416. Springer (2016)
Biggio, B., Fumera, G., Russu, P., Didaci, L., Roli, F.: Adversarial biometric recognition: a review on biometric system security from the adversarial machine-learning perspective. IEEE Sig. Process. Mag. 32(5), 31–41 (2015)
Biggio, B., Fumera, G., Russu, P., Didaci, L., Roli, F.: Poisoning adaptive biometric systems. In: Structural, Syntactic, and Statistical Pattern Recognition, pp. 417–425. Springer, Heidelberg (2012)
Li, S.Z., Jain, A.: Encyclopedia of Biometrics, 2nd edn. Springer, New York (2015)
Rajeswari, P., Viswanadha Raju, S., Ashour, A.S, Dey, N.: Multi-fingerprint unimodel-based biometric authentication supporting cloud computing. In: Intelligent Techniques in Signal Processing for Multimedia Security, pp. 469–485. Springer, Cham (2017)
Jeng, R.-H., Chen, W.-S.: Two feature-level fusion methods with feature scaling and hashing for multimodal biometrics. IETE Tech. Rev. 34(1), 91–101 (2017)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)
Castrillón-Santana, M., Lorenzo-Navarro, J., Ramón-Balmaseda, E.: Multi-scale score level fusion of local descriptors for gender classification in the wild. Multimedia Tools and Appl. 76(4), 4695–4711 (2017)
Parkavi, R., Babu, K.R.C., Kumar, J.A.: Multimodal biometrics for user authentication. In: 2017 11th International Conference on Intelligent Systems and Control (ISCO), pp. 501–505. IEEE (2017)
El-Sayed, M.A., Estaitia, Y.A., Khafagy, M.A.: Automated edge detection using convolutional neural network. Int. J. Adv. Comput. Sci. Appl. 4(10), 10–20 (2013)
Peralta, D., Triguero, I., García, S., Saeys, Y., Benitez, J.M., Herrera, F.: On the use of convolutional neural networks for robust classification of multiple fingerprint captures. Int. J. Intell. Syst. 33(1), 213–230 (2018)
Anitha, T.N., Ravi, J., Geetha, K.S., Raja, K.B.: Bimodal biometric system using multiple transformation features of fingerprint and iris. Int. J. Inf. Technol. (ACEEE) 1(3), 20 (2011)
Gerig, G., Kubler, O., Kikinis, R., Jolesz, F.A.: Nonlinear anisotropic filtering of MRI data. IEEE Trans. Med. Imaging 11(2), 221–232 (1992)
Weickert, J.: Multiscale texture enhancement. In: Computer Analysis of Images and Patterns, pp. 230–237. Springer (1995). https://doi.org/10.1007/978-3-642-40246-3
Fingerprint verification Competition. WWW document (2006)
Gorodnichy, D.O.: Evolution and evaluation of biometric systems. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications, CISDA 2009, pp. 1–8. IEEE (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-74690-6_61
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-74689-0
Online ISBN: 978-3-319-74690-6
eBook Packages: EngineeringEngineering (R0)