NDLI: A recognition approach using multilayer perceptron and keyboard dynamics patterns

Content Provider	IEEE Xplore Digital Library
Author	Rezaei, A. Mirzakuchaki, S.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Iran Univ. of Sci. & Technol., Tehran, Iran (Rezaei, A.; Mirzakuchaki, S.)
Abstract	Multilayer perceptron (MLP) with one hidden layer is one of the most common forms of artificial neural networks ever utilized. A well-trained MLP with proper number of nodes in its hidden layer is demonstrated to have efficient and robust performance on patterns with high orders. In this paper in order to form an identification system, MLP is utilized as a classifier to distinguish keyboard dynamics patterns of several people. A variant number of neurons in the single hidden layer is investigated empirically to reach the optimum number. The optimum number of hidden layer neurons has been found to be 44 and relevant equal error rate (EER) equal to 0.95% has been reported. The false acceptance rate (FAR) and false reject rate (FRR) for this number of neuron has been empirically evaluated equal to 0.49% and 19.51% respectively.
Starting Page	1
Ending Page	5
File Size	227001
Page Count	5
File Format	PDF
ISBN	9781467362047
e-ISBN	9781467362061
DOI	10.1109/PRIA.2013.6528445
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-03-06
Publisher Place	Iran
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Back propagation (BP) Multilayer perceptron (MLP) Biometrics (access control) Neurons Keyboard dynamics Authentication Artificial neural networks Multilayer perceptrons Artificial neural network (ANN) Identification system
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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