Master of Science (MS)
Fabio Di Troia
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.
Sundaravaradhan, Preethi, "Smartphone Gesture-Based Authentication" (2019). Master's Projects. 701.