## 3 Literature Review
### Physiological Biometrics
#### 3.2.2 Ocular Dynamics  
Ocular dynamics are a type of physiological biometric that have been investigated by researchers for their use in mobile authentication. Many studies [86, 87, 88, 89] have focused on both ocular and periocular regions of an individual's eye to discriminate users. In study [86], researchers proposed a novel heterogeny-aware deep-embedding scheme for mobile periocular recognition. The architecture for this scheme involves a CNN in which the periocular images pass through. During training, a heterogeny-aware loss function optimizes feature representations to reduce intra-class variations. To train their model, three different datasets containing periocular imaging were used. Results found that their model outperformed comparison models with 99.41% accuracy and 1.32% EER with the VISOB dataset. Out of the reviewed studies using the VISOB dataset, this scheme produced the best EER values as shown in Table 5. OcularNet, a CNN-based ocular recognition model, was proposed in study [87]. This scheme utilizes a periocular region detected via eye landmark localization. Six overlapping patches are extracted from the periocular region, from which a CNN model is trained as a multiclass classifier for each patch. Four different datasets containing ocular imaging are used to train their models. Both ResNet \begin{table}
\begin{tabular}{c c c} \hline \hline
**Study** & **Algorithm** & **EER (\%)** \\ \hline
[78] & CNN + SVM & 0.20 \\
[85] & SVM & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of studies with UMD-AA01 and MOBIO facial datasets [85, 8]. Figure 8: Figure depicting the proportion of architectures used in the cited facial authentication schemes. and OcularNet architectures are compared on their performance. Results found that despite being a smaller model, OcularNet was able to outperform ResNet, producing a lowest mean EER of 1.17% with the Oppo N1 device and the VISOB dataset. Researchers of [88], following the success of VISOB 1.0, present their findings from their most recent competition in mobile ocular biometric recognition. This publication first proposes their new VISOB 2.0 dataset, consisting of selfie image capturing of the periocular eye region from 150 volunteers for the training set and 100 for the testing set. For the selfie images, three sets of lighting were taken in burst mode, from which stacks of five images were used in the dataset. For the competition and evaluation, three teams participated. Team 1 used five ResNet-CNN models pretrained on the UGGFace dataset and fine-tuned with VISOB 2. Team 2 utilized decision tree-local binary patterns (DT-LBP), the only non-DL approach. Team 3 used GoogleNet extraction and LSTM prediction. Results from this study found that the deep learning approaches achieved better results. Specifically, the ResNet-CNN team achieved the best results with 5.256% EER and 0.988 AUC with Note4 challenge data. Researchers of [89] presented an in-depth study comparing multiple deep architectures for iris/ocular mobile authentication. Many CNN architectures such as VGG, ResNet, DenseNet, MobileNetV1, MobileNetV2 were compared with NasNet-Mobile and their own proposed architecture. Their proposed model was based off MobileNetV2, with a drop in spatial resolution and an increase in feature channels, resulting in a model with the least number of features and parameters. This model was modified to reduce computational complexity while retaining a mobile-capable authentication system. The VISOB dataset was reduced into three sections with around 200-400 participants each to suit their training and testing needs. Overall, it was found that ResNet-50, DenseNet-50, and the proposed model performed consistently better than the other comparison models. The study claimed their proposed model to be the best trade-off between performance and computational cost. In the subject-independent open set scenario, their model has the best EER values ranging from 4.65 to 6.57% EER.