Frequency spectrograms for biometric keystroke authentication using neural network based classifier

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• 作者：Orcan Alpar ^{orcanalpar@hotmail.com}
• 关键词：Biometrics ; Spectrogram ; Frequency ; Keystroke authentication ; Short-time Fourier transformation ; Gauss-Newton based neural networks
• 刊名：Knowledge-Based Systems
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：116
• 期：Complete
• 页码：163-171
• 全文大小：1987 K
• 卷排序：116

文摘

This paper deals with a novel frequency based authentication method and a Gauss-Newton based Neural Network classifier. The purpose of this research is to provide the foundations of frequency authentication to enhance keystroke authentication protocols. We presented short time Fourier transform to analyze the train signal of keystrokes. We also analyzed the spectrograms to discriminate various signals. EER of the proposed feature extraction and classification method is found as 4.1%.