# CoreTemp: Coreset Sampled Templates for Multimodal Mobile Biometrics
## 5 Experiments
### Identification on HMOG and BBMAS  
We further present our identification experiment in two ways. Denoting that we are the first to attempt identification utilizing touchstroke-gait behavioral biometrics, we acknowledge that such an experiment poses a significant challenge as behavioral biometrics inherently exhibits comparatively lower correlation with the user's identity, and registration and search involving multiple templates of various users may be time- and resource-consuming, especially on mobile devices. To this end, we have proposed PIEformer+ for generating robust templates, coreset sampling, and fast matching for efficiency. Acknowledging that we are the first to attempt identification with touchstroke-gait signals, we test for various backbones, namely, LSTM, Transformer, and PIEformer, where the number of parameters has been sensibly balanced; 5-stacked LSTM with a hidden state dimension of 160, 4-layers of Transformer encoders with eight multi-heads and a dimension of embedding and forward at 128 and 512, and the number of learnable inputs for PIEformer and PIEformer+ set at 8 with the rest of the parameters shared with the Transformer. We present the experimental results with Rank1 and Rank5 values in Table 4, with a further delivery of the CMC curve in Figure 4. A detailed implementation of the experiment is provided in Algorithm 2. \begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline \multicolumn{2}{l}{**(\%), _Except AUROC_} & \multicolumn{4}{c}{**BBMAS \(\rightarrow\) HMOG**} & \multicolumn{4}{c}{**HMOG \(\rightarrow\) BBMAS**} \\ \cline{3-10} \multicolumn{2}{l}{**Model**} & **Coreset** & **Fast Match** & **EER (\(\downarrow\))** & **AUROC (\(\uparrow\))** & **TPR@FPR1\% (\(\uparrow\))** & **[EMAIL_ADDRESS]\% (\(\uparrow\))** & **EER (\(\downarrow\))** & **AUROC (\(\uparrow\))** & **TPR@FPR1\% (\(\uparrow\))** \\ \hline \multirow{3}{*}{Transformer} & & & \(10.51_{+6.32}\) & 0.9361\({}_{+0.0947}\) & 76.86\({}_{+18.39}\) & 61.19\({}_{+24.07}\) & 4.60\({}_{+2.57}\) & 0.9852\({}_{+0.0305}\) & 77.63\({}_{+14.91}\) \\  & ✓ & & \(10.76_{+6.00}\) & 0.9337\({}_{+0.0991}\) & 74.89\({}_{+19.930}\) & 58.35\({}_{+22.60}\) & 4.90\({}_{+2.73}\) & 0.9834\({}_{+0.0293}\) & 76.33\({}_{+14.69}\) \\  & ✓ & ✓ & \(10.76_{+6.00}\) & 0.9336\({}_{+0.0991}\) & 74.89\({}_{+19.930}\) & 58.34\({}_{+23.60}\) & 5.01\({}_{+2.33}\) & 0.9667\({}_{+0.0232}\) & 75.53\({}_{+12.76}\) \\ \hline \multirow{3}{*}{PIEformer} & & & \(9.22_{+6.08}\) & 0.9460\({}_{+0.1082}\) & 80.72\({}_{+0.038}\) & 64.31\({}_{+23.28}\) & 3.66\({}_{+18.98}\) & 0.9983\({}_{+0.0187}\) & 79.91\({}_{+11.87}\) \\  & ✓ & & \(9.70_{+6.22}\) & 0.9430\({}_{+0.1048}\) & 79.49\({}_{+19.61}\) & 63.69\({}_{+2.92}\) & 4.17\({}_{+11.89}\) & 0.9863\({}_{+0.109}\) & 87.65\({}_{+10.98}\) \\  & ✓ & ✓ & \(10.18_{+6.36}\) & 0.9386\({}_{+0.1109}\) & 77.61\({}_{+22.73}\) & 60.92\({}_{+25.84}\) & 4.52\({}_{+2.40}\) & 0.9842\({}_{+0.0266}\) & 76.87\({}_{+11.24}\) \\ \hline \multirow{3}{*}{PIEformer+} & & & \(8.97_{+5.83}\) & 0.9554\({}_{+0.0974}\) & 81.90\({}_{+20.36}\) & 64.47\({}_{+23.07}\) & 3.52\({}_{+2.13}\) & 0.9890\({}_{+0.0227}\) & 79.61\({}_{+12.92}\) \\  & ✓ & & \(9.23_{+6.28}\) & 0.9468\({}_{+0.1088}\) & 80.60\({}_{+0.244}\) & 64.39\({}_{+23.87}\) & 3.74\({}_{+2.10}\) & 0.9883\({}_{+0.0216}\) & 79.52\({}_{+12.23}\) \\ \cline{1-1}  & ✓ & ✓ & \(9.63_{+6.47}\) & 0.9432\({}_{+0.1119}\) & 78.63\({}_{+22.46}\) & 62.35\({}_{+24.61}\) & 4.03\({}_{+2.17}\) & 0.9859\({}_{+0.0231}\) & 77.44\({}_{+11.65}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the PIEformer+ with other attention-based architectures for authentication, namely, Transformer and PIEformer. BBMAS \(\rightarrow\) HMOG indicates BBMAS as the pretraining set, whereas HMOG is utilized for inference, and vice versa. Figure 3: The averaged ROC curve for intra-dataset experiments, as a more detailed presentation of Table 2. Best viewed in color.