## 3 Literature Review
### Physiological Biometrics
#### 3.2.1 Facial Dynamics
In phase 2, LeNet-5 CNN classifies the user's eye-blinking sequence. Datasets CEW and ZJU, consisting of eye photos and blinking videos, are used to train their eye-blinking classification model. Batch normalization was used to increase classification accuracy. Their model was compared with KNN and CNN + SVM models and outperformed with an FI score of 98.4% on the CEW dataset and an operation speed of 20 frames per second. In [82], researchers propose a mobile face authentication scheme via deep CNN with mobile cloud computing. The architecture of this model includes CNN + local ternary pattern (LTP) for feature extraction and KNN for classification. Facial images captured on the mobile device are stored on the cloud, which are prerocessed by LTP and sent to train the DCNN. The information is decrypted via partially homomorphic encryption (PHE), and the cloud makes the authentication decision, which is sent back to the device. Five facial datasets, including ORL, Yale Face database and the Georgia Tech Face database, are used to train and evaluate the model. The LTP-DCNN was able to achieve high performance in accuracy, precision, recall, and FI for all databases, outperforming existing methods. Recognition accuracy under encrypted data averaged 90.90-98.78% between the datasets. Some studies utilized non-CNN architectures and algorithms for facial authentication and achieved comparable results [83, 84, 85]. A two-step face authentication scheme (TSFAS) was proposed in [83]. TSFAS utilizes a combination of facial biometrics and pin entry to enhance mobile banking security. The model begins once the user unlocks their device, with video feed monitoring their interactions. Model architecture involves facial feature extraction via denoised autoencoder (DAE). DAE is also used to perform the classification task. To train and evaluate TSFAS, the MSU-MFSD database was used, consisting of 280 video clips from 35 users as well as attack attempts. Results found that TSFAS was able to outperform two-faction authentication and hierarchical correlation-based authentication methods, with the best performing true rejection rates (TRRs) exceeding 92.58%. TSFAS was also able to resist spoofing attacks provided by the MSU-MFSD database. Study [84] proposed a generic prototype for a facial biometric mobile authentication model that preserves user privacy when working with services and transactions. Three components make up this model: the mobile device, the identity provider (IDP), and the service provider (SP). The IDP is the software that performs enrollment and authentication, while the service provider is the client application that facilitates the transaction. This protocol allows mobile users to authenticate to online services while preserving their privacy. The architecture for the authentication model included a C-SVM for multiclass classification. To evaluate their model, the AT&T Laboratories Cambridge dataset was utilized. After many trails with different numbers of enrolled users in the authentication system and random users in the trained algorithm, their model was able to reach acceptable FAR and FRR levels. With a ratio of 1:2 enrolled and random users, the model was able to achieve an FAR of \(\sim\)1%. In [85], researchers presented a continuous mobile authentication scheme with facial attributes biometrics. The PubFig database, consisting of facial images with labeled attributes, was used to train the attribute classifiers. This training process involves first detecting facial landmarks. Then, facial components are extracted. For each component, features are extracted and then dimensionally reduced with PCA. These features are used to train the classifiers. SVM with RBF kernel was chosen as a classifier and compared with many other methods using the MOBIO and AA01 mobile video face datasets. Linear-SVM on a 0.5 scale was able to achieve a best EER of 0.25%, outperforming the LBF method. The proposed model was implementable on a mobile scenario with acceptable memory usage, power consumption, and speed.