Touch Analysis: An Empirical Evaluation of Machine Learning
  Classification Algorithms on Touch Data
Melodee Montgomery Prosenjit Chatterjee John Jenkins Kaushik Roy
Our research aims at classifying individuals based on their unique
interactions on touchscreen-based smartphones. In this research, we use
Touch-Analytics datasets, which include 41 subjects and 30 different behavioral
features. Furthermore, we derived new features from the raw data to improve the
overall authentication performance. Previous research has already been done on
the Touch-Analytics datasets with the state-of-the-art classifiers, including
Support Vector Machine (SVM) and k-nearest neighbor (kNN), and achieved equal
error rates (EERs) between 0% to 4%. Here, we propose a novel Deep Neural Net
(DNN) architecture to classify the individuals correctly. The proposed DNN
architecture has three dense layers and uses many-to-many mapping techniques.
When we combine the new features with the existing ones, SVM and kNN achieved
the classification accuracy of 94.7% and 94.6%, respectively. This research
explored seven other classifiers and out of them, the decision tree and our
proposed DNN classifiers resulted in the highest accuracy of 100%. The others
included: Logistic Regression (LR), Linear Discriminant Analysis (LDA),
Gaussian Naive Bayes (NB), Neural Network, and VGGNet with the following
accuracy scores of 94.7%, 95.9%, 31.9%, 88.8%, and 96.1%, respectively.