## 3 Literature Review
### Physiological Biometrics
#### 3.2.1 Facial Dynamics
The proposed approach was compared with multiple baselines, including SVM and OC-NN. Overall, the OC-ACNN approach had superior performance compared to other OC classification methods on all three databases and all three extractor networks. VGGFace extractor achieved the best results with the OC-ACNN model on the UMDAA-01 database with 0.9772 \(\pm\) 0.0213 AUROC. While CNN alone can provide state-of-the-art mobile authentication, many studies have paired CNN with other ML algorithms in a hybrid model architecture [78, 79, 80, 81, 82]. Study [78] presents a multitask model for mobile authentication via facial attributes. Two CNN-based architectures are proposed: deep CNNAA and wide CNNAA. Both architectures consist of an ensemble of multitask DCNNs intended to extract facial attributes, with parameters varying depending on the facial region which is operated. Attributes extracted from the CNN architectures are then predicted by linear SVM. Two publicly available mobile-captured facial video and image datasets, MOBIO and AA01, are used for the active authentication evaluation problem. Results found that MutliDeep-CNNAA had better performance since the MultiWide-CNNAA had more parameters and overfitted to celebrity facial images. Overall, their attribute detection models performed better than comparison methods. In the active authentication problem, their method was able to outperform previous facial attribute studies. MutliDeep-CNNAA achieved an EER of 0.19-0.20% with the datasets tested. Between the reviewed studies using the MOBIO and A011 datasets, MutliDeep-CNNA presents the best EER performance as shown in Table 4. Echoprint, a mobile facial authentication system using both acoustic and visual features, was proposed in [79]. Echoprint utilizes the frontal camera to capture visual face features. For acoustic features, inaudible sound is emitted from the device to illuminate the physical face and capture features from the resulting echo. The architecture for this model included CNN feature extraction and SVM classification. Forty-five users supplied the data for their training set, as well as five nonhuman images to provide sufficient data. Twelve users joined for model evaluation. Echoprint achieved a mean precision score of 98.05% when the 12 new users were evaluated on the model and were able to resist spoofing attacks. When comparing the usage of different features with different classifiers, it was found that SVM outperformed with CNN features, and that CNN features had the best accuracy rate overall. In [80], researchers trained and evaluated a small-sized mobile facial recognition model. Three deep learning solutions were compared for feature extraction in this dataset: FaceNet (CNN), OpenFace, and gb2s_model. Distance-based classifier (DBC) and SVM were compared as classifiers. To sufficiently train and test a model suited to scenarios as realistic as possible, a large sum of facial images was acquired from nine different public databases. Results found that DL solutions performed better than geometrical and local binary patterns (LBPs). The largest model, FaceNet, was able to achieve the best results with less variability between datasets as compared to the other models. When using SVM matching, system performance improved as compared to DBC. With SVM matching, FaceNet achieved an EER ranging from 0.00 to 2.56%. Ultimately, it is concluded that the feature extracting model directly influences results, and that the deeper the model, the better the results. Researchers of [81] created a model relying on facial recognition and eye-blinking biometrics to improve dynamic mobile authentication. Two phases make up their proposed model, with facial recognition performed in phase 1, and eye-blink authentication performed in phase 2. In phase 1, the face is detected via a histogram of oriented gradients (HOG) and linear SVM, from which facial landmarks are obtained. A CNN uses these landmarks to extract facial features, and an SVM performs classification.