Machine learning based soft biometrics for enhanced keystroke recognition system

The proposed work investigates the performance enhancement of keystroke biometric recognition using soft biometric with filter and Score Boost Weighting (SBW) scheme. Usually, Keystroke recognition performance is lower due to user’s emotional behaviour or distraction, typing patterns vary from user normal position which causes recognition error of genuine user for degrading the recognition accuracy. To address this problem, this work presents Dual Matcher with fusion to reduce the false rejection of genuine user to improve the accuracy of keystroke recognition. In this paper, soft biometric is used as secondary information to improve the recognition accuracy for primary keystroke biometric system. Specifically, soft biometrics provides additional support for keystroke biometric recognition at the combination approach. The performance of keystroke system can be further improved using SVM as machine learning under the score level fusion in the combination approach. Lastly, the fusion technique is used to combine the primary and secondary biometric. The new approach with score fusion enhances the overall performance of keystroke biometric system with 99% accuracy. Maximum of 2% improvement is achieved compared to existing works.