## 3 Literature Review
### Behavioral Biometrics
#### 3.1.1 Touch Dynamics
In [12], scrolling and motion sensor-based touch data were evaluated with a DL model for two-class classification. A DNN consisting of three dense layers with either 64 or 128 nodes in each layer was used as a classifier. Scrolling and motion sensor data from the Hand Movement, Orientation and Grasp (HMOG) dataset was used for training. Their model was evaluated on performance with different combinations of scrolling and motion sensor features. It was found that while more features did not guarantee better performance, pairing some motion sensor data with scroll data resulted in a better performance than scroll data alone. Gyroscope and scroll features combined resulted in the best performance with 80-81% average precision and 89-90% average accuracy. Other studies using the HMOG dataset are compared based on performance in Table 1. In [13], DL architecture was evaluated with their touch-stroke-based continuous authentication model, smart continuous authentication system (SCAS). The Touchalytics dataset containing touchscreen interaction and navigation data from 41 participants was used. Their DL architecture consisted of a first layer, which took portions of each stroke event as input data for their algorithms, and two hidden layers involving leaky rectified linear unit (Leaky ReLu) activation functions at each layer and SoftMax activation function at the output layer. Their model was able to achieve a testing accuracy of up to 94% with an average EER of 3.4%. In studies [14, 15], researchers chose multilayer perceptron (MLP) as a classification algorithm. Researchers of [14] evaluated a touch-and-motion sensor-based continuous authentication scheme. For their model, random forest was used to detect the motion state of the user, and MLP was used to authenticate the user with a model trained from their detected motion state. A cloud-based server was used to facilitate the registration and continuous authentication phased for their model. For model training and testing, the study used an unsupervised dataset consisting of touch and motion sensor data from 20 participants. Results found that their model with MLP classification was able to achieve an accuracy of 95.1% while using accelerometer and gyroscope data. MLP was able to outperform three other comparison algorithms. In [15], researchers proposed TouchMetric, an application designed to test machine learning algorithms with touch dynamics authentication. For their evaluation data, a thirty-four-subject dataset consisting of touch activity data from three categories (move activity, swipe activity, and type activity) was used to train and evaluate their models. Results from this study found that MLP was the only classifier of six to correctly classify both authenticated and unauthenticated users. Studies [16, 17, 18, 19] used other types of deep learning architectures and algorithms for classification. In [16], researchers proposed their touch-stroke-based two-class authentication model, kernel deep regression network (KDRN). Their proposed KDRN utilized stacking-based representation learning, consisting of multiple hierarchical layers of kernel ridge regression (KRR) trained analytically and independently. For training and testing, the Touchalytics dataset consisting of vertical and horizontal touch strokes from 41 participants was used. When compared with MLP, SVM, and radial basis function (RBF) kernel KRR, their KDRN model achieved the best results in all scenarios with an intra-session EER of 0.01 + -0.02%. KDRN produced the best EER performance between reviewed studies using the Touchalytics dataset as demonstrated in Table 2. For study [17], researchers proposed a continuous authentication scheme utilizing touch gesture dynamics. In this scheme, a radical basis function network (RBFN) with particle swarm optimization (PSO) was used for classification. PSO + RBFN was compared to four other classifiers, including decision tree (DT) J84, JRip, back propagation neural network (BPNN), and naive Bayes (NB).