JILSA  Vol.8 No.2 , May 2016
Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning
Abstract: Fingerprint recognition is a mature biometric technique for identification or authentication application. In this work, we describe a method based on the use of neural network to authenticate people who want to accede to an automated fingerprint system for E-learning. The idea is to apply back propagation algorithm on a multilayer perceptron during the training stage. One of the advantages of this technique is the use of a hidden layer which allows the network to make comparison by calculating probabilities on template which are invariant to translation and rotation. Results come both from the NIST special database 4 and a local database, and show that a proposed method gives good results in some cases.
Cite this paper: Kouamo, S. and Tangha, C. (2016) Fingerprint Recognition with Artificial Neural Networks: Application to E-Learning. Journal of Intelligent Learning Systems and Applications, 8, 39-49. doi: 10.4236/jilsa.2016.82004.

[1]   Jain, A.K., Feng, J.J. and Nandakumar, K. (2010) Fingerprint Matching. IEEE Computer Society, 43, 36-44.

[2]   Maltoni, D., Maio, D., Jain, A.K. and Prabhakar, S. (2003) Handbook of Fingerprint Recognition. Springer, New York.

[3]   Chatterjee, A., Mandal, S., Atiqur Rahaman, G.M. and Mohammad Arif, A.S. (2010) Fingerprint Identification and Verification System by Minutiae Extraction Using Artificial Neural Network.

[4]   Sathiaraj, V. (2012) A Study on the Neural Network Model for Finger Print Recognition. International Journal of Computational Engineering Research, 2, 70.

[5]   Galton F. (1892) Fingerprint. McMillan and Co., London.

[6]   Kouamo, S. and Tangha, C. (2012) Handwritten Character Recognition with Artificial Neural Network. Distributed Computing and Artificial Intelligence. Advances in Intelligent and Soft Computing, 151, 535-543.

[7]   Sakshica, Gupta, K., Vidyapith, B., Campus, J. and Jaipur (2015) Handwritten Digit Recognition Using Various Neural Network Approaches. International Journal of Advanced Research in Computer and Communication Engineering, 4, 4.

[8]   Jagtap, V.N. and Mishra, S.K. (2014) Fast Efficient Artificial Neural Network for Handwritten Digit Recognition. International Journal of Computer Science and Information Technologies, 5, 2302-2306.

[9]   International Biometric Group (2011) The Henry Classification.

[10]   Yu, L., Laaraiedh, M., Avrillon, S. and Uguen, B. (2011) Fingerprint Localisation Based on Neural Networks and Ultra-Wide Band Signals. IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, 14-17 December 2011, 184-189.

[11]   Xia, X. and O’Gorman, L. (2003) Innovations in Fingerprint Capture Devices. Pattern Recognition, 36, 361-369.

[12]   Li, R., Li, C.T. and Guan, Y. (2015) A Compact Representation of Sensor Fingerprint for Camera Identification and Fingerprint Matching. IEEE International Conference on Acousticsi Speech and Signal Processing (ICASSP), Brisbane, 19-24 April 2015, 1777-1781.

[13]   Wiilis A.J. and Myers, L. (2001) A Cost-Effective Fingerprint Recognition System for Use with Low-Quality Prints and Damaged Fingertips. Pattern Recognition, 34, 255-270.

[14]   Ojha, A.K. (2015) ATM Security Using Fingerprint Recognition. International Journal of Advanced Research in Computer Science and Software Engineering, 5, 170-175.

[15]   Cappelli, R., Lumini, A., Maio, D. and Maltoni, D. (2002) Synthetic Fingerprint-Database Generation. Proceeding of the 16th International Conference on Pattern Recognition, 3, 744-747.

[16]   Baldi, P. and Chauvin, Y. (1993) Neural Networks for Fingerprint Recognition. Neural Computation, 5, 402-418.

[17]   Hamsa, A.A. (2012) Fingerprint Identification System Using Neural Networks. Nahrain University, College of Engineering Journal (NUCEJ), 15, 234-244.

[18]   Thomas, T.J. (2000) Locally-Connected Neural Network for Fingerprint Recognition. Proceedings of the ISATED International Conference, Intelligent Systems and Control, Honolulu.

[19]   Kouamo, S. and Tangha, C. (2013) Images Compression with Artificial Neural Network. Advances in Intelligent and Systems and Computing, 189, 515-524.

[20]   Batchakui, B., Tangha, C. and Kameni, J.S. (2012) xCCM: An Alternative Method of Appropriate Contents Creation on an e-Learning Platform. IEEE Global Engineering Education Conference (EDUCON), Morocco, 17-20 April 2012, 1-7.

[21]   Watson, C.I. and Wilson, C.L. (1992) NIST Special Database 4 Figerprint Database. National Institute of Standards and Technology, Advanced Systems Division, Image Recognition Group.