Abstract
This paper illustrates a multibiometric method to optimize the fusion of multiple biometries at the score level. The fused score is a linear combination of the individual scores. As a consequence, well-known traditional linear optimization techniques become suitable to determine the constants to be used in the linear combination. The proposed method uses training to optimize the constants. After experimenting with dummy datasets, a fresh multi-biometric dataset of infrared images has been prepared. The data has been subject to extra distortion and occlusions, and then used to train first the individual biometric systems, based on GoogleNet CNNs, and then the fusion engine. Results obtained through the proposed method have an accuracy over 99% in the best configuration. The system at present performs user verification, but an extension to identification can be obtained by reworking the constraints in the optimization problem. A sketch of such extension is provided.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Hosseini, S.: Fingerprint vulnerability: a survey. In: IEEE, 2018 4th International Conference on Web Research (ICWR). https://doi.org/10.1109/ICWR.2018.8387240
Galbally, J., Marcel, S., Fierrez, J.: Biometric antispoofing methods: a survey in face recognition. IEEE Access. 2, 1530–1552 (2014). https://doi.org/10.1109/ACCESS.2014.2381273
De Marsico, M., Distasi, R., Nappi, M., Riccio, D.: Fractal indexing in multimodal biometric contexts. In: Kocarev, L., Galias, Z., Lian, S. (eds.) Intelligent Computing Based on Chaos. Studies in Computational Intelligence, vol. 184. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-540-95972-4_5
De Marsico, M., Distasi, R., Nappi, M., Riccio, D.: Multiple traits for people identification: face, ear and fingerprints. In: Sencar, H.T., Kocarev, L., Galias, Z., Lian, S. (eds.) Intelligent Multimedia Analysis for Security Applications. Studies in Computational Intelligence, vol. 282, pp. 79–98. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-11756-5_4
Singh, S.A.: Review on multibiometrics: classifications, normalization and fusion levels. In: IEEE, 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE). https://doi.org/10.1109/ICACCE.2018.8441727
Jaafar, H., Ramli, D.A.: A review of multibiometric system with fusion strategies and weighting factor. Int. J. Comput. Sci. Eng. (IJCSE) 2(4), 158–165 (2013)
Imran, M., Rao, A., Kumar, G.H.: A new hybrid approach for information fusion in multibiometric systems. In: IEEE, 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics. https://doi.org/10.1109/NCVPRIPG.2011.57
Talreja, V., Valenti, M.C., Nasrabadi, N.M.: Multibiometric secure system based on deep learning. In: 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP). https://doi.org/10.1109/GlobalSIP.2017.8308652
Rattani, A., Reddy, N., Derakhshani, R.: Multi-biometric convolutional neural networks for mobile user authentication. In: 2018 IEEE International Symposium on Technologies for Homeland Security (HST). https://doi.org/10.1109/THS.2018.8574173
Nair, V.S., Reshmypriya, G.N., Rubeena, M.M., Fasila, K.A.: Multibiometric cryptosystem based on decision level fusion for file uploading in cloud. In: IEEE, 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT). https://doi.org/10.1109/ICRAECT.2017.19
Li, C., Hu, J., Pieprzyk, J., Susilo, W.: A new biocryptosystem-oriented security analysis framework and implementation of multibiometric cryptosystems based on decision level fusion. IEEE Trans. Inf. Forensics Secur. 10(6), 1193–1206 (2015). https://doi.org/10.1109/TIFS.2015.2402593
Sharma, R., Das, S., Joshi, P.: Rank level fusion in multibiometric systems. In: IEEE, 2015 Fifth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG). https://doi.org/10.1109/NCVPRIPG.2015.7489952
Kadri, F., Meraoumia, A., Bendjenna, H., Chitroub, S.: Palmprint & iris for a multibiometric authentication scheme using Log-Gabor filter response. In: IEEE, 2016 International Conference on Information Technology for Organizations Development (IT4OD). https://doi.org/10.1109/IT4OD.2016.7479287
Kabir, W., Ahmad, M.O., Swamy, M.N.S.: Score reliability based weighting technique for score-level fusion in multi-biometric systems. In: 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). https://doi.org/10.1109/WACV.2016.7477580
Patil, A.P., Bhalke, D.G.: Fusion of fingerprint, palmprint and iris for person identification. In: IEEE, 2016 International Conference on Automatic Control and Dynamic Optimization Techniques (ICACDOT). https://doi.org/10.1109/ICACDOT.2016.7877730
Abate, A.F., Nappi, M., Riccio, D., De Marsico, M.: Face, ear and fingerprint: designing multibiometric architectures. In: 14th International Conference on Image Analysis and Processing (ICIAP 2007) (2007)
Abate, A.F., Nappi, M., Ricciardi, S.: Smartphone enabled person authentication based on ear biometrics and arm gesture. In: 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Abate, A.F., Bisogni, C., Castiglione, A., Distasi, R., Petrosino, A. (2019). Optimization of Score-Level Biometric Data Fusion by Constraint Construction Training. In: Wang, G., El Saddik, A., Lai, X., Martinez Perez, G., Choo, KK. (eds) Smart City and Informatization. iSCI 2019. Communications in Computer and Information Science, vol 1122. Springer, Singapore. https://doi.org/10.1007/978-981-15-1301-5_14
Download citation
DOI: https://doi.org/10.1007/978-981-15-1301-5_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1300-8
Online ISBN: 978-981-15-1301-5
eBook Packages: Computer ScienceComputer Science (R0)