Abstract
Recognition and authentication are important factors for implementation in every computerized system. This particularly plays a significant role in electronic banking and luxurious cars. PIN code or key can be lost or stolen by an imposter. Therefore, the characteristics of humans are the best recognition points to authenticate a user. Artificial Neural Network (ANN) is the only computational network which works as the working of human brain and its neurons function by adopting the features of a human. In this research, we have proposed an algorithm for training of fingerprint biometric system by implementing Artificial Neural Networks for the recognition of finger features of the human. The method includes detection of minutiae values of the ridge termination and bifurcation points. The multilayer feed forward network is the successful network with error back propagation algorithm for pattern recognition through supervised learning. This network is being used in many applications of recognition and control. This architecture is applicable for finger minutiae extraction for recognition of car user and its features through memory settings. This network gives 99% correct classification for recognition of the user.
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Rafay, A., Hasan, Y., Iqbal, A. (2019). Recognition of Fingerprint Biometric System Access Control for Car Memory Settings Through Artificial Neural Networks. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-03405-4_26
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DOI: https://doi.org/10.1007/978-3-030-03405-4_26
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