## 4 Limitations  
Despite the promising results and insightful contributions of the various studies presented in this paper, many have limitations in their data collection and research conduction that could affect the legitimacy and relevancy of their proposed models. One common limitation occurring in many studies reviewed would be the usage of small datasets. While it can be difficult to accumulate or find datasets relevant to a study with large amounts of data, it is significantly important to the quality of modern authentication schemes. As DL becomes more relevant in the study of biometrics authentication, the need for larger datasets has increased. DL algorithms require a substantial amount of data to adequately train their models in comparison to ML models. Small datasets also affect data quality in that significant data variability can lead to harmful bias and underrepresentation of the target population. This results in a model that cannot accurately classify the target population. Another frequent limitation would be a lack of testing against attacks. While some studies such as [30; 31; 44; 79; 83; 98] were engaged in testing their models against attacks, a vast majority did not. There are many different types of attacks designed to combat various types of mobile authentication strategies, such as smudge, over-the-shoulder, replica, puppet, mimicry, replay, and impersonation attacks. As mobile authentication schemes become more advanced, it is important to make sure our models can effectively resist these types of attacks. Another limitation prevalent in many studies would be constrained scenarios in data collection and authentication testing. In the cases of many behavioral biometric schemes such as gait, touch, and motion dynamics, there can be a lot of variability in how the individual uses their device and performs an activity. Often, these data are difficult to process for authentication. While some studies such as [7; 8; 35; 65] account for this variability in their data collection and preprocessing stages and allow unconstrained data collection and realistic smartphone usage, many studies instruct their volunteers to follow more rigid instructions for data collection and testing. This can result in models \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dynamic** & **Study** & **Algorithms** & **Performance** & **Datasets** \\ \hline \multirow{8}{*}{Ocular} & [86] & CNN & 99.41\% ACC & VISOB Dataset: 550 subjects, \\  & & & & CSIP Dataset: 50 subjects, \\  & [87] & CNN & 1.17\% EER & IIITD Dataset: 62 subjects \\  & [87] & CNN & 1.17\% EER & VISOB Dataset: 550 subjects, \\  & & & & UBIRIS-1 Dataset: 241 subjects, \\  & & & & UBIRIS-2 Dataset: 261 subjects, \\  & & & & CrossEyed Dataset: 120 subjects \\  & [88] & CNN & 0.988 AUC & VISOB-2 Dataset; 150 subjects \\  & [89] & CNN & 4.65–6.57\% EER & VISOB Dataset: 550 subjects \\  & [90] & CNN + ANN & 0.03–0.06\% EER & IIITD Dataset: 225 subjects, \\  & [91] & CNN & 99.10\% ACC & MUDataset: 45 subjects \\  & [92] & CNN & 91.8–93\% ACC & UTiris Dataset: 79 subjects \\ \hline \multirow{8}{*}{Other} & [93] & CNN & 91.8–93\% ACC & NCUT-FR Dataset: 5 k images, \\  & [94] & CNN + LOF & 97.99\% BAC & MST-DB4 Dataset: 4 k images \\  & [94] & CNN & \textgreater{}99.00\% ACC & Original Dataset: 105 subjects \\  & & & & FVS2006, ATVSFrDB, Spoof Attack Finger Vein \\  & & & & Database, LivDet 2013 Dataset, LivDet 2015 Dataset \\  & [95] & CNN & 0.03\% EER & UWA Benchmark, ManTech Phase2, \\  & & & & PolyU, MSU, IIT, ISPFDv2, ZJU Datasets: \textasciasciell k \\  & [96] & CNN & 0.33\% EER & UC3M-CV2: 2400 images, \\  & & & & UC3M-CV1: 1200 images, \\  & & & & PUT: 1200 images, \\  & & & & ImageNet: 14 million images \\  & [97] & Naive-Bayes & 94\% ACC & Original Dataset: 11 subjects \\  & [98] & NN & 98.31\% ACC & Original Dataset: 61 subjects \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._that are not equipped to handle authentication in real-life scenarios.