## 3 Literature Review  
### Behavioral Biometrics  
#### 3.1.1 Touch Dynamics  
Deep learning algorithms have been used to make classification decisions for many touch-based mobile authentication models. For studies [7; 8; 9], CNN architectures were used for classification. In [7], researchers used their proposed feature representation tactic, multiple channels biological graph (MCBG), with CNN to enhance their continuous mobile authentication scheme. MCBG takes touch and motion sensor data patterns and visualizes them into one of three feature graphs. These graphs are taken as input to train a CNN algorithm for classification. For model training and testing, a dataset of real-world touch interaction data from 180 participants was used. Their proposed model achieved a 96.77% accuracy with both moving and stationary data from the dataset. When compared with other classification algorithms, CNN had the best authentication accuracy with each feature graph input. Researchers of [8] evaluated implicit touch authentication with multidimensional touch and motion sensor data. For their classifier, CNN was used for its high performance with continuous implicit authentication (CIA) schemes. To use sequence data as CNN inputs, SE_ResNet with an attention mechanism was utilized to convert time series information into multichannel data. Datasets used for model training and testing included their own uncontrolled unlock pattern touch dataset with 50 users and the controlled BrainRun public dataset, consisting of various touch data collected from 2 k users via five interaction games. In the controlled scenario, an average accuracy of 96.94% was achieved, and in the uncontrolled scenario, a one-time recognition accuracy of 87.11% was achieved. These results provide evidence for the effectiveness of touch and motion sensor data with CIA. In [9], researchers analyzed deep models with touch dynamics authentication. For their model architecture, feature regularization net CNN was used to extract features from touch behavioral images, and a bidirectional LSTM was used to extract features from touch behavioral sequences. These features were then regularized and fused into a single layer before being sent to a K-nearest neighbor (KNN) algorithm for classification. For model training and evaluation, a multitouch gesture dataset was used, involving four-finger touch interaction data from 161 participants. The full version of their model, involving handcrafted feature concatenation and regularization and embedding loss, was able to achieve an equal error rate (EER) of 1.7%, outperforming comparison models. Studies [10; 11] chose LSTM as a classifier. Researchers of [10] analyzed a touch mobile authentication system with simple linear touch gestures. For their authentication model, a Siamese neural network architecture was used for classification that consisted of two LSTM layers. They trained their model with right swipe gesture data from the 600 subject HuMIdb database. From this touch data, they used 11 temporal features. They compared their model's performance with a Gaussian kernel binary-SVM. Results found that their model's performance saturated at six samples with 13% EER and outperformed the comparison SVM model. For [11], researchers presented their database MobileTouchDB and analyzed a passcode-drawing-based mobile authentication model. MobileTouchDB consisted of unsupervised touch data from 217 users assigned to draw numbers and characters on their device. A Siamese LSTM with dynamic time warping (DTW) was chosen for their classification model. During experimentation, the discriminative power of different characters as well as the effect of the length of passwords was evaluated. Their LSTM classification model was compared to four other systems. Results found that their time-aligned RNN achieved the best EER at 2.38%, and that EER decreased as password length increased until a length of four. DNN architectures were used in studies [12; 13].