Publication Date

Spring 2019

Degree Type

Master's Project

Degree Name

Master of Science (MS)


Computer Science

First Advisor

Mark Stamp

Second Advisor

Katerina Potika

Third Advisor

Fabio Di Troia


Gesture Based Authentication, CNNs, SVMs


In this research, we consider the problem of authentication on a smartphone based on gestures, that is, movements of the phone. Accelerometer data from a number of subjects was collected and we analyze this data using a variety of machine learning techniques, including support vector machines (SVM) and convolutional neural networks (CNN). We analyze both the fraud rate (or false accept rate) and insult rate (or false reject rate) in each case.