Acceleration Axis Selection in Biometric Technique Based on Gesture Recognition

This article proposes a biometric technique based on gesture recognition performed directly in a mobile device embedding an accelerometer. As time consumption is an essential requirement, this article aims to discover the most distinctive acceleration axis information in order to find the best strategy considering EER and consumed time. Best EER result of 2.5% has been obtained when the information of accelerations on each of the three axis is analyzed. When reducing the information inspected to only two or one acceleration axis signals, EER values are 2.98% and 4.34% respectively. Preprocessing various acceleration signals by calculating their magnitude outcomes with higher EER values. All this work has been developed from a database of 34 individuals who have performed their identifying gestures, and three falsifiers who have attempted to forge each original in-air signatures from studying video records.

[1]  Webb Miller.  An Introduction to Bioinformatics Algorithms. Neil C. Jones and Pavel A. Pevzner , 2006 .

[2]  P. Suganthi,et al.  Mobile Phones Security Using Biometrics , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[3]  Webb Miller.  An Introduction to Bioinformatics Algorithms , 2006 .

[4]  R. Stephenson.  A and V , 1962, The British journal of ophthalmology.

[5]  Yanggon Kim,et al.  Pupil and Iris Localization for Iris Recognition in Mobile Phones , 2006, Seventh ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing (SNPD'06).

[6]  A. de Santos Sierra,et al.  A fuzzy DNA-based algorithm for identification and authentication in an Iris Detection System , 2008, 2008 42nd Annual IEEE International Carnahan Conference on Security Technology.

[7]  Feng Hao,et al.  Combining Crypto with Biometrics Effectively , 2006, IEEE Transactions on Computers.

[8]  Sungzoon Cho,et al.  Keystroke dynamics-based authentication for mobile devices , 2009, Comput. Secur..

[9]  Arun Ross,et al.  Handbook of Biometrics , 2007 .

[10]  W. Marsden.  I and J , 2012 .

[11]  Christopher Verplaetse,et al.  Inertial Proprioceptive Devices: Self-Motion-Sensing Toys and Tools , 1996, IBM Syst. J..

[12]  Raymond N. J. Veldhuis,et al.  Biometric Authentication for a Mobile Personal Device , 2006 .

[13]  John Daugman,et al.  Face and Gesture Recognition: Overview , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  N. Ramaraj,et al.  Enhancing the Efficiency of Regression Testing through Intelligent Agents , 2007, International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007).

[15]  M.E. Moghaddam,et al.  On-line signature verification using ANFIS , 2009, 2009 Proceedings of 6th International Symposium on Image and Signal Processing and Analysis.