Biometric Security Systems Using Neural Networks
Keywords:
biometric security, neural networks, facial recognition, fingerprint analysis, authenticationAbstract
This study investigates the use of neural networks to enhance the accuracy and reliability of biometric security systems. Focusing on facial recognition and fingerprint analysis, the research evaluates different neural network architectures to improve identification and authentication processes. The experimental approach highlights the potential for neural networks to reduce false acceptance and rejection rates, thus increasing security levels in sensitive environments. By integrating artificial intelligence with biometrics, new opportunities for safeguarding personal information are uncovered. The study provides a framework for future improvements in biometric system reliability and performance.
References
Darvishy, A. (2014). Accessibility of mobile platforms. In Design, User Experience, and Usability. User Experience Design Practice: Third International Conference, DUXU 2014, Held as Part of HCI International 2014, Heraklion, Crete, Greece, June 22-27, 2014, Proceedings, Part IV 3 (pp. 133-140). Springer International Publishing.
Darvishy, A., Leemann, T., & Hutter, H. P. (2012). Two software plugins for the creation of fully accessible PDF documents based on a flexible software architecture. In Computers Helping People with Special Needs: 13th International Conference, ICCHP 2012, Linz, Austria, July 11-13, 2012, Proceedings, Part I 13 (pp. 621-624). Springer Berlin Heidelberg.
Satyanarayana, D., & Elmirghani, J. M. (2010, December). An energy efficient network architecture for infrastructured wireless networks. In 2010 IEEE Global Telecommunications Conference GLOBECOM 2010 (pp. 1-6). IEEE.
Satyanarayana, D., & Elmirghani, J. M. (2009, September). A voronoi based energy efficient architecture for wireless networks. In 2009 Third International Conference on Next Generation Mobile Applications, Services and Technologies (pp. 377-382). IEEE.
Satyanarayana, D., & Rao, S. V. (2008, July). Local Delaunay triangulation for mobile nodes. In 2008 First International Conference on Emerging Trends in Engineering and Technology (pp. 282-287). IEEE.
Satyanarayana, D., Sathyashree, A. A. K., & Alkalbani, A. (2015, November). Voronoi LEACH for Energy Efficient Communication in Wireless Sensor Networks. In Proceedings for the International Conference on Electronics Computer networks and Information Technology, Dubai.
Kumar, N., & Kataria, V. Enhanced Sentiment Classification using a Multi-layered Stacked Ensemble Architecture.
Rahimov, E. R. (2010). BASE PRINCIPAL OF MANAGING OF NETWORK SOFTWARE SECURITY BY VULNERABILITIES DETERMINATION MODEL. Computer Sciences and Telecommunications, (5), 70-74.
