Using Support Vector Machines To Distinguish Users Through Touch Gesture Recognition

Security and authentication are essential components of modern-day computing systems. Yet the advantages of traditional passwords do not extend to touchscreen technology where others can easily discern passwords (Kim et al. 2010) or the passwords are shorter and therefore less secure (Findlater et al. 2011). Research has provided fruitful authentication alternatives for touchscreens, such as facial recognition and fingerprint scanning, but these have limited adoption on these devices. Gesture recognition is a biometric identification method with much potential for adoption onto touchscreen devices yet limited research in the subject area.;I offer a two-factor gesture identification system for the Microsoft PixelSense Table. The program incorporates a gesture symbol ("what you know" password) that also identifies a user based on their behavioral-based biometric password inputs on a touchscreen device ("what you are" password). Eighteen features were extracted from the raw data and incorporated into a support vector machine, a supervised machine learning algorithm that classifies users based on their consistent input of a touch password. In addition to the program, I also conduct data collection of 19 participants and their unique gestures to determine the most distinct feature set for our model. I provide a proof of concept that gestures can be used as passwords on the PixelSense table that avoids many of the problematic elements of alphanumeric passwords on touchscreens. I also provide a new list of features that can be incorporated into future learning models on other touchscreen devices.