## 3 Literature Review
### Physiological Biometrics
#### 3.2.3 Other Physiological Dynamics
data from large amounts of subjects, such as the HMOG, IDNet, MOBIO, VISOB, and HuMldb datasets. A great number of studies are achieving high metrics in accuracy and low metrics in EER values, demonstrating the potential for biometric mobile authentication security. Overall, the table represents a trend of high performance among studies evaluating biometric mobile authentication models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dynamic** & **Study** & **Algorithms** & **Performance** & **Datasets** \\ \hline  & [7] & CNN & 97.00\% ACC & Original Dataset: 180 subjects \\  & [8] & CNN & 96.94\% ACC & Original Dataset: 50 subjects, \\  & [9] & CNN + LSTM + KNN & 1.7\% EER & Outsourced Dataset: 161 subjects \\  & [10] & LSTM-RNN & 13\% EER & HuMldb Dataset: 600 subjects \\  & [11] & LSTM-RNN & 2.38\% EER & e-BioDigitDB: 93 subjects, \\  & & & & Mobile TouchDB: 217 subjects \\  & [12] & DNN & 89–90\% ACC & HMOG Dataset: 100 subjects \\  & [13] & DNN & 3.4\% EER & Touchalytics Dataset: 41 subjects \\  & [14] & MLP & 95.1\% ACC & Original Dataset: 20 subjects \\  & [15] & MLP & 100\% ACC & Original Dataset: 34 subjects \\  & [16] & KRR & 95\% ACC & Touchalytics Dataset: 41 subjects \\  & [17] & PSO + RBFN & 1.95\% AER & Original Dataset: 20 subjects \\ Touch & [18] & AC-GAN & Median 7\% EER & Touchalytics Dataset: 41 subjects \\  & [19] & DAE-SR & 0.03\% EER & HMOG Dataset: 10 subjects used \\  & [20] & OC-SVM + K-means & 93.7\% TPR & Original Dataset: 64 subjects \\  & [21] & OC-SVM & 98.96\% ACC & Original Dataset: 77 subjects \\  & [22] & SVM + GMM & 3\% EER & Serwadda, Frank, Antal, UMDAA-02 Datasets: \\  & & & & 350 subjects total \\  & [23] & SVM & 2.9\% AER & Original Dataset: 10 participants \\  & [24] & SVM & 97.7\% ACC & Original Dataset: 10 subjects \\  & [25] & RF & 0.002 \(\pm\) 0.000 EER & Original Dataset: 40 subjects \\  & [26] & RF & 91.79\% TAR & Original Dataset: 95 subjects \\  & [27] & RF & \textgreater{}0.1\% EER & Original Dataset: 14 subjects \\  & [28] & GBC & 0.9692 AUC & Original Dataset: 14 subjects \\  & [29] & L-GBC & 0.913–0.921\% AUC & Original Dataset: 9 k subjects \\  & [30] & GMM & 91.5\% ACC & Original Dataset: 20 subjects \\ \hline  & [31] & CNN & 90–94\% ACC & Original Dataset: 16 subjects \\  & [32] & CNN + OC-SVM & 90.04\% ACC & Original Dataset: 100 subjects \\  & [33] & CNN + OC-SVM & 97.8\% ACC & HMOG Dataset: 100 subjects \\  & [34] & CNN + SVM & 96.72\% ACC & HMOG Dataset: 100 subjects \\  & [35] & CNN + SVM & 95.01\% ACC & Original Dataset: 1513 subjects \\  & [36] & CNN + OC-SVM & 1.00\% EER & Original Dataset: 100 subjects \\  & [37] & LSTM & 0.09\% EER & Original Dataset: 84 subjects \\  & [38] & LSTM & 0.01\% EER & HMOG Dataset: 100 subjects, \\  & & & & UCI-HAR Dataset: 30 subjects, \\  & & & & WISDM-HARB Dataset: 51 subjects \\  & [39] & LSTM-RNN & 99.05\% AUC & Original Dataset: 41 subjects \\ Motion & [40] & RNN + GMM & 18.17\% EER & Original Dataset: 15.5 subjects \\  & [41] & DAE & 2.2\% EER & HMOG Dataset: 100 subjects, \\  & & & & Original Dataset: 20 subjects \\  & [42] & DNN & 94–98\% ACC & Original Dataset: 19 subjects \\  & [43] & SVM & 97.95\% ACC & Original Dataset: 10 subjects \\  & [44] & SVM & 95.57\% ACC & Outsourced Dataset IV: 1513 subjects \\  & [45] & SVM & 76.85\% ACC & Sherlock Dataset: 52 subjects \\  & [46] & SVM & 92.0\% ACC & Original Dataset: 60 subjects \\  & [47] & RF & 97.13\% ACC & HAR Dataset: 30 subjects, \\  & & & & PAMAP2 Dataset: 9 subjects, \\  & & & & MobiAct Dataset: 59 subjects \\  & [48] & RF & 99.35\% ACC & Original Dataset: 85 subjects \\ \hline \hline \end{tabular}
\end{table}