# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 6 Conclusions  
This paper proposed a mobile continuous authentication method using the touch-gesture signals acquired from the touch screen, accelerometer, and gyroscope. We posit continuous mobile authentication as a few-shot anomaly detection problem utilizing a novel sequential data augmentation technique and network architecture. We proposed random pattern mixing to diversify impostor features for the training by producing class-unconstrained impostor patterns. Moreover, we presented the local feature pooling-based TCN, a variant of the temporal convolutional network (TCN), aggregating global and local feature information to learn delicate impostor patterns from sensor data. Accordingly, our proposed method can characterize various fine-grained impostor patterns from limited seen impostor samples, gaining high robustness against unseen impostors. Finally, we demonstrated practical continuous authentication based on the decision and score-level fusion. They constantly improve the accuracy performance against extended re-authentication time without causing the long-term dependency problem or increasing model complexity. Our experiments showed the effectiveness of our method compared to baselines and its state-of-the-art performance over the recent seven mobile authentication methods.  
Despite the state-of-the-art performance of our method, there is room for further improvement. First, unlike the classical cross entropy loss, a more powerful loss function could be explored, especially for anomaly detection problems. Secondly, how to transfer knowledge from different domains could be investigated to utilize additional prior knowledge of impostors. Lastly, one could design a novel decision-making framework tailored for continuous authentication.  
Conceptualization, K.W. and A.B.J.T.; methodology, K.W.; software, K.W.; validation, K.W. and A.B.J.T.; formal analysis, K.W.; investigation, K.W.; resources, K.W.; data curation, K.W.; writing--original draft preparation, K.W.; writing--review and editing, K.W. and A.B.J.T.; visualization, K.W.; supervision, A.B.J.T.; project administration, A.B.J.T.; funding acquisition, A.B.J.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NO. NRF-2022R1A2C1010710).  
Not applicable.  
This study leveraged two publicly available datasets, the HMOG [37] and BBMAS [38] datasets. The HMOG dataset is downloadable from [[https://www.cs.wm.edu/~qyang/hmog.html](https://www.cs.wm.edu/~qyang/hmog.html)]([https://www.cs.wm.edu/~qyang/hmog.html](https://www.cs.wm.edu/~qyang/hmog.html)) (accessed on 20 August 2022). The BBMAS dataset is downloadable from [[https://ieee-dataport.org/open-access/su-ais-bb-mas-syracuse-university-and-assured-information-security-behavioral-biometrics](https://ieee-dataport.org/open-access/su-ais-bb-mas-syracuse-university-and-assured-information-security-behavioral-biometrics)]([https://ieee-dataport.org/open-access/su-ais-bb-mas-syracuse-university-and-assured-information-security-behavioral-biometrics](https://ieee-dataport.org/open-access/su-ais-bb-mas-syracuse-university-and-assured-information-security-behavioral-biometrics)) (accessed on 20 August 2022).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Re-Authentication Time** & **Sensor-Level Fusion** & **Parameter Size** \\ \hline
1 s & 10.95 & 0.5 M \\
3 s & 10.20 & 0.7 M \\
5 s & 9.64 & 0.7 M \\
7 s & 9.68 & 0.8 M \\ \hline \hline \end{tabular}
\end{table}
Table 6: Averaged EERs (\(\%\)) of the sensor-level fusion for different re-authentication time.