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.

翻译：本研究旨在通过对用户在触摸屏智能手机上的独特交互行为进行个体识别。我们使用包含41名受试者及30项不同行为特征的Touch-Analytics数据集，并从原始数据中衍生出新特征以提升整体认证性能。此前研究已基于该数据集采用支持向量机（SVM）与k近邻（kNN）等先进分类器，取得了0%至4%的等错误率（EER）。本文提出一种新型深度神经网络（DNN）架构以实现个体精准分类。该DNN架构包含三个全连接层，并采用多对多映射技术。当新特征与原有特征结合时，SVM与kNN分别达到94.7%和94.6%的分类准确率。本研究额外探索了七种分类器，其中决策树与本文提出的DNN分类器取得了100%的最高准确率。其余分类器包括：逻辑回归（LR）、线性判别分析（LDA）、高斯朴素贝叶斯（NB）、神经网络及VGGNet，其准确率分别为94.7%、95.9%、31.9%、88.8%和96.1%。