Combining Keystroke Dynamics and Face Recognition for User Verification

TL;DR

This paper presents a user verification system combining keystroke dynamics and face recognition, achieving high accuracy and leveraging common hardware, thus offering a secure, unobtrusive, and cost-effective authentication method.

Contribution

It introduces a fusion of keystroke dynamics and face recognition using Hidden Markov Models and Eigenfaces, enhancing biometric verification reliability.

Findings

False Acceptance Rate of 5.4%

False Rejection Rate of 9.2%

High precision in user verification

Abstract

The massive explosion and ubiquity of computing devices and the outreach of the web have been the most defining events of the century so far. As more and more people gain access to the internet, traditional know-something and have-something authentication methods such as PINs and passwords are proving to be insufficient for prohibiting unauthorized access to increasingly personal data on the web. Therefore, the need of the hour is a user-verification system that is not only more reliable and secure, but also unobtrusive and minimalistic. Keystroke Dynamics is a novel Biometric Technique; it is not only unobtrusive, but also transparent and inexpensive. The fusion of keystroke dynamics and Face Recognition engenders the most desirable characteristics of a verification system. Our implementation uses Hidden Markov Models (HMM) for modelling the Keystroke Dynamics, with the help of two widely used Feature Vectors: Keypress Latency and Keypress Duration. On the other hand, Face Recognition makes use of the traditional Eigenfaces approach.The results show that the system has a high precision, with a False Acceptance Rate of 5.4% and a False Rejection Rate of 9.2%. Moreover, it is also future-proof, as the hardware requirements, i.e. camera and keyboard (physical or on-screen), have become an indispensable part of modern computing.