Review on impact of Face Recognition Technology in Biometrics
Keywords:
Biometrics, Simulation, Physical TraitsAbstract
Proposed research gives pilot results of project that is oriented on use of Neural Network in Face recognition. Biometrics is the technology of identifying uniquely human subjects by means of measuring and analyzing one or more intrinsic behavioral or physical traits. These human body characteristics include fingerprints, voice patterns, eye retinas and irises, facial patterns and hand measurements. Neural networks have been successfully applied in a wide range of supervised and unsupervised learning applications. Neural-network methods are not commonly used for data-mining tasks, however, because they often produce incomprehensible models and require long training times. We have integrated Neural-Network in this research with Biometric technique known as Face Recognition.
References
J. Liu, S. Zhang, Y. Ye, Agent-based characterization of web regularities, in N. Zhong, et al. (eds.), Web Intelligence, NewYork: Springer, 2003, pp. 19–36.
J. Liu, N. Zhong, Y. Y. Yao, Z. W. Ras, wisdom web: new challenges for web intelligence (WI), J. Intell. Inform. Sys.,20(1): 5–9, 2003.
Congiusta, A. Pugliese, D. Talia, & P. Trunfio, Designing GridServices for distributed knowledge discovery, Web Intell. Agent Sys, 1(2): 91–104, 2003.
J. A. Hendler & E. A. Feigenbaum, Knowledge is power: semantic web vision, in N. Zhong, et al. (eds.), Web Intelligence: Research & Development, LNAI 2198, Springer, 2001, 18–29.
N. Zhong & J. Liu (eds.), Intelligent Technologies for Information Analysis, New York: Springer, 2004.
Bouckaert, Remco R.; Frank, Eibe; Hall, Mark A.; Holmes, Geoffrey; Pfahringer, Bernhard; Reutemann, Peter; Witten, Ian H. (2010). "WEKA Experiences with a Java open-source project".
Journal of Machine Learning Research 11: 2533–2541. original title, "Practical machine learning", was changed ... term "data mining" was [added] primarily for marketing reasons.
Mena, Jesús (2011). Machine Learning Forensics for Law Enforcement, Security, & Intelligence. Boca Raton, FL: CRC Press
(Taylor & Francis Group). ISBN 978-1-4398-6069-4.
Piatetsky-Shapiro, Gregory; Parker, Gary (2011). "Lesson: data/information Mining, & Knowledge Discovery: An Introduction". Introduction to data/information Mining. KD Nuggets. Retrieved 30 August 2012.
Kantardzic, Mehmed (2003). data/information Mining: Concepts, Models, Methods, & Algorithms. John Wiley & Sons. ISBN 0-471-22852-4. OCLC 50055336.
"Microsoft Academic Search: Top conferences in data/information mining". Microsoft Academic Search.
"Google Scholar: Top publications - data/information Mining & Analysis". Google Scholar.
Proceedings, International Conferences on Knowledge Discovery & data/information Mining, ACM, New York.
SIGKDD Explorations, ACM, New York
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2016 International Journal for Research Publication and Seminar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.