Abstract
As technology is getting advanced day by day, the concern of security, authentication, and identification are also becoming important in every domain. Apart from other identifiers used for authentication, a biometric identifier is a quantitative assessment of a person’s bodily characteristics, which is effectively used to authenticate or corroborate the identification. Biometric identity is significantly more difficult to forge by attackers. With different biometric identification systems like fingerprint identification, face detection etc., Iris recognition is also an essential and most efficient biometric identification system which uniquely identifies a person’s identity. Also, the data corresponding to Iris identification is very sensitive and is supposed to be used in a very secure manner. The work aims here is to develop a privacy preserving and powerful Convolution Neural Networks (CNN) model for Iris recognition in a federated learning approach. The said approach provides privacy to the user data because there is no sharing of data with the central server, unlike the traditional machine learning approach where data from all clients are required to be stored on central server. The work implemented in this paper simulated the CNN model in a privacy-preserving manner. The result provides the performance of the model for different combinations of participating clients and global rounds.
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References
Gupta, H., Patel, D., Makade, A., Gupta, K., Vyas, O., Puliafito, A.: Risk Prediction in the Life Insurance Industry Using Federated Learning Approach. In: 2022 IEEE 21st Mediterranean Electrotechnical Conference (MELECON), pp. 948–953 (2022)
Hu, K., Xia, M., Lu, M., Weng, L.: Federated Learning: A Distributed Shared Machine Learning Method. Hindawiy. 47, 1–20 (2009)
Umer, S., Dhara, B.C., Chanda, B.: A Noble Cance-lable Iris Recognition System Based on Feature Learning Technique. Elsevier Information Sci. 406 (2017)
Naseema, I., Aleemb, A., Togneric, R., Bennamoun, M.: Iris recognition using class-specific dictionaries. Elsevier Comput. Electr. Eng. 62, 178–193 (2016)
Galdi, C., Nappi, M., Dugelay, J.: Multimodal authentication on smartphones: Combining iris and sensor recognition for a double check of user identity. Patt. Recogn. Lett. 82144–153 (2016), https://www.sciencedirect.com/science/article/pii/S0167865515003190, An insight on eye biometrics
Azam, M., Rana, H.: Iris Recognition using Convolutional Neural Network. Int. J. Comput. Appl. 175, 24–28 (2020). http://www.ijcaonline.org/archives/volume175/number12/31505-2020920602
Albawi, S., Mohammed, T., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference On Engineering And Technology (ICET), pp. 1–6 (2017)
Omelina, L., Goga, J., Pavlovicova, J., Oravec, M., Jansen, B.: A survey of iris datasets. Image Vision Comput. 108 1–20 (2021)
Mishra, V., Kumar, S., Shukla, N.: Image Acquisition and Techniques to Perform Image Acquisition. SAMRIDDHI : A J. Phys. Sci. Eng. Technol. 9, (2017)
Zuo, J., Ratha, N., Connell, J.: A new approach for iris segmentation. In: 2008 IEEE Computer Society Conference On Computer Vision And Pattern Recognition Workshops, CVPR Workshops, pp. 1–6 (2008,7)
Verma, P., Dubey, M., Verma, P.: Hough Transform Method for Iris Recognition-A Biometric Approach. Int. J. Eng. Innov. Technol. (IJEIT). 1 (2012)
Daugman, J.: High condence visual recognition of persons by a test of statistical independence. Patt. Anal. Mach. Intell. IEEE Trans. 1 (1993)
Li, Q., et al.: A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protection. CoRR. abs/1907.09693 (2019), http://arxiv.org/abs/1907.09693
Aledhari, M., Razzak, R., Parizi, R., Saeed, F.: Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications. IEEE Access. 8 1–1 (2020,1)
Ahmed, L., Ahmad, K., Said, N., Qolomany, B., Qadir, J., Al-Fuqaha, A.: Active learning based federated learning for waste and natural disaster image classification. IEEE Access. 8, 208518–208531 (2020)
Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., Ilie-Zudor, E.: Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study. Appl. Sci. 8 2663 (2018,12)
Lyu, L., Yu, H., Yang, Q.: Threats to Federated Learning: A Survey. CoRR. abs/2003.02133 (2020). https://arxiv.org/abs/2003.02133
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Gupta, H., Rajput, T.K., Vyas, R., Vyas, O.P., Puliafito, A. (2023). Biometric Iris Identifier Recognition with Privacy Preserving Phenomenon: A Federated Learning Approach. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Communications in Computer and Information Science, vol 1794. Springer, Singapore. https://doi.org/10.1007/978-981-99-1648-1_41
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