Identity Authentication based on Keystroke Dynamics using Genetic Algorithm and Particle Swarm Optimization
Techniques based on biometrics have been successfully applied to personal identification systems. Keystroke dynamics is a promising biometric technique to recognize an individual based on an analysis of his/her typing patterns. In this work, Mean and Standard Deviation of Latency, Duration and Digraph is measured as keystroke features. Optimization techniques such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used for feature subset selection and their performance is compared. Particle Swarm Optimization gave moderate performance than Genetic Algorithm. Using the duration as the feature for feature subset selection is novel.
Feature Eztraction Feature Subset Selection Mean and Standard Deviation Genetic Algorithm and Particle Swarm Optimization
Marcus Karnan M. Akila
Dept. of Computer Science and Engineering Tamilnadu College of Engineering Coimbatore, India Dept. of Computer Science and Engineering Anna University Coimbatore, Coimbatore, India
国际会议
北京
英文
2960-2964
2009-08-08（万方平台首次上网日期，不代表论文的发表时间）