Cost-Effective Kernel Ridge Regression Implementation for Keystroke-Based Active Authentication System

Abstract

In this paper, a fast kernel ridge regression (KRR) learning algorithm is adopted with O(N) training cost for largescale active authentication system. A truncated Gaussian radial basis function (TRBF) kernel is also implemented to provide better cost-performance tradeoff. The fast-KRR algorithm along with the TRBF kernel offers computational advantages over the traditional support vector machine (SVM) with Gaussian-RBF kernel while preserving the error rate performance. Experimental results validate the cost-effectiveness of the developed authentication system. In numbers, the fast-KRR learning model achieves an equal error rate (EER) of 1.39% with O(N) training time, while SVM with the RBF kernel shows an EER of 1.41% with O(N^2) training time.