## 3 Literature Review
### Physiological Biometrics  
#### 3.2.1 Facial Dynamics  
Capturing facial features has been a popular method of mobile authentication in recent years. Many studies [64, 74, 75, 76, 77] have utilized the CNN algorithm for their authentication models. In [74], a cloud-based mobile face authentication system is proposed. On the user side, the device owner captures a face picture which is resized and sent to the cloud for authentication. On the cloud side, CNN is used for feature extraction and Softmax performs the classification. A total of 2800 facial images from 200 individuals in the FEI Face database was used for training and evaluation. The model achieved an accuracy of 99.50% and 0.01% loss. Efficient mask-net, a CNN-based facial authentication model, was proposed in [75]. The model architecture used a CNN for feature extraction and large margin piecewise linear classifier (LMPL) for classification. This system intended to authenticate users with and without face masks. To authenticate masked users, GAN was employed to generate a full face from the masked image, from which the system authenticates. Images from Masked Face-Net and Flicker Face-HQ datasets were used to train their model. Results found that their model was able to outperform other face-mask authentication models, with 99.53-99.64% accuracy with EfficientNetB0 + LMPL. In study [64], researchers address  
Figure 6: Depiction of a human gait cycle [70]. Figure 7: Gyroscope signals of three volunteers [73]. the issue of face detection methods losing accuracy as the face distance is closer to the camera. They present a CNN-based mobile frontier face detection model. This method compared [PERSON] and CNN for face detection and alignment. The CNN FaceNet model was then employed for facial verification. This model was evaluated on a dataset of 40 individuals captured via a front-facing camera in three different distances. Results found that CNN outperformed [PERSON] for facial detection with a recognition accuracy of \(\sim\)0.97. When compared with SOTA models such as PCA, latent Dirichlet allocation (LDA), Fisherface, and [PERSON]'s face, CNN outperformed with \(\sim\)0.99 verification accuracy. For [76], researchers present MobileFaceNets, a class of real-time CNN face verification systems. For their architectures, residual bottlenecks from MovieNetV2 are used as building blocks. PreLu is used as a nonlinearity. The primary MobileFaceNet has a large computational cost and 0.99 million parameters, so MobileFaceNet-M and MobileFaceNet-S were constructed as well. MobileFaceNet-M removes a linear 1 \(\times\) 1 convolutional layer after the linear GDConv layer, and MobileFaceNet-S removes a linear 1 \(\times\) 1 convolutional layer before the linear GDConv as well. The MobileFaceNets were compared to baselines such as MobileNetV, SuffleNet, and MobileNetV2. All models were trained on the CASIA-WebFace dataset and tested on the Labeled Faces in the Wild (LFW) and Age DB-30 datasets. The MobileFaceNets achieved an overall better performance compared to baseline studies, with a highest accuracy of 99.28% on the LFW dataset with the original MobileFaceNet. Models M and S were able to achieve extremely close results with less parameters and smaller computational cost. Researchers in study [77] propose a one class autoencoder regularized CNN (OC-ACNN) for active face authentication. The architecture consists of three nodes, feature extraction, classification, and decoder networks. In the feature extraction network, any CNN architecture can be used as the base, but AlexNet, VGGFace, and VGG16 were used for evaluation purposes. Before feeding input into the classification network, it is concatenated with a Gaussian vector to act as a pseudo-negative class. The classification network consists of a one-layer connected classification network. The decoder network consists of a four-layer CNN. The entire network is trained with two loss functions and three face datasets: MOBIO, UMDAA-01, and UMDAA-02.