## 3 Literature Review
### Behavioral Biometrics
#### 3.1.4 Gait Dynamics  
Deep learning algorithms have been used in many studies on gait-based mobile authentication schemes. CNN-based models [63; 64; 65] have been used in gait dynamics authentication. IDNet, a gait mobile authentication scheme, was proposed in [63]. This model utilized a CNN for universal feature extraction and an OC-SVM for classification. Accelerometer and gyroscope data were collected from 50 subjects over a six-month period  
Figure 5: PCA visualization of the dataset. Each student is represented by a different color [62]. to form their dataset. IDNet was built to be able to classify gait cycles regardless of smartphone orientation. For evaluation, CNN features were tested with multiple classifiers, and their IDNet was compared to other studies. Results found that all classifiers performed better with CNN features as opposed to manually selected features and that CNN features with SVM classifier had the best performance. Overall, IDNet outperformed the comparison literature with less than 0.15% FRR and FAR in fewer than five walking cycles. In [64], researchers explored the use of deep metric learning with gait-based mobile authentication. In this study, three CNN architectures (LeNet, VGG, and MobileNetv2) were compared based on their authentication performance with different Siamese networks (baseline, Siamese multiclass, Siamese binary-class). Four different datasets consisting of gait sensor data ranging from 20 users to 744 users were utilized to train the models. Results found that all three architectures had the best authentication performance with Siamese binary class and joint loss function. A best accuracy of 0.981% was achieved on the ZJU dataset with VGG8 architecture. For study [65], a model identification model using CNN and gait dynamics was proposed for smartphone-based sensing applications. This system extracts statistical and discrete Fourier transform (DFT) features from accelerometer data, which are then used to train a CNN model for identification. The Kaggle dataset, a real-world benchmark dataset consisting of accelerometer data collected from 387 users, was used in this study. For evaluation, their CNN model was compared to SVM, RF, DT, LR, and KNN. CNN performed the best on average with a highest accuracy (0.9882 \(\pm\) 0.004), precision, recall, and F1. Many studies [66, 67, 68] chose LSTM as a viable algorithm to pair with gait mobile authentication schemes. In study [66], a gait mobile authentication method utilizing three-axis accelerometer data was proposed. For this model, feature extraction was performed in the preprocessing stage. A hybrid deep recurrent neural network (DRNN) with a hidden LSTM layer was used for classification. A twenty-one-subject dataset consisting of flatland walking data in two smartphone-holding positions was used to train their model. For evaluation, the performance of their model was tested in different architecture parameters as well as between the two smartphone-holding positions (pocket and handholding). Results found that in handholding position, increasing the number of LSTM blocks resulted in a better classification rate and that two hidden layers was optimal for rate and evaluation time. For gait identification, their LSTM model performed better than RF. For gait authentication, their model was able to achieve a higher than 95% accuracy for almost all subjects in pocket position and 90% or higher in handholding position. Researchers of [67] continued their previous research on their smartphone imposter detection (SID) model, focusing on deep learning algorithms and protecting user data privacy. This study utilized the WALK section of the HAPT dataset, consisting of motion sensor data collected from 30 participants with smartphones strapped to their waists. Their model was evaluated in two scenarios: IDaaS, a binary classification scenario, and LAD, a scenario in which only user data are available for training. LSTM and SVM were compared in the LAD scenario and MLP was compared to SVM in the IDaaS scenario.