# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 2 Related Works
### Deep Anomaly Detection in Mobile-Based Biometrics  
Mobile-based biometric authentication is often formulated as an anomaly detection problem. Here, we limit the scope to deep learning-based anomaly detection models. Based on how a model is trained, the existing models can be broadly categorized into unsupervised, semi-supervised, or fully-supervised ones. Table 1 tabulates several recent deep anomaly detection models in mobile-based biometrics. Most methods utilize motion sensor data, such as the accelerometer, gyroscope, and magnetometer. Many works utilize behavior data while walking [3; 9; 16] or operating a smartphone [2; 4; 5; 6; 7; 8; 10; 17].  
Autoencoder is a popular architecture for unsupervised learning [2; 3]. This approach uses an encoder network for input dimension reduction and a decoder network to reconstruct inputs and compute reconstruction losses as anomaly scores.  
The semi-supervised models are composed of a pretrained CNN feature extractor, dimensionality reduction, and a one-class classifier such as OC-SVM [4; 5; 6; 7]. Many of them are designed as light-weighted models, accounting for the deployment in mobile devices [4; 5; 6]. An advantage of this approach is that classifiers can leverage powerful representations from pretrained feature extractors. However, the training of the feature extractors is completely decoupled from that of the classifiers. Therefore, the learned representations are not optimized for the classifiers. Besides, the disuse of impostor data yields decision boundaries without characterizing impostor patterns, often failing to detect impostors when genuine or unlabeled data have large intra-class variances.  
The other approach is fully supervised anomaly detection, in which a large set of genuine and labeled impostor data are given at the training. Existing works have mainly utilized CNNs [16], LSTM [3; 8], and ConvLSTM [9; 17]. Using the large-scale labeled impostor data, they reduce an anomaly detection problem to a binary (a genuine user with a positive label vs. the rest with a negative label) or multi-class (a genuine user vs. multiple impostors) classification problem. The use of impostor data can significantly boost the model performance [3]. However, the availability of large impostor data is often unrealistic  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sensor** & **Supervision** & **Architecture** \\ \hline [PERSON] et al. [2] & Acc. 1  & Unsupervised & Autoencoder \\ [PERSON] et al. [3] & Acc. 1 [FOOTNOTE:1]Footnote 1: Accelerometer, 2 Gyroscope, 3 Magnetometer, 4 Touch screen, 5 Principal component analysis, 6 Factor analysis.  
due to time and monetary costs. In particular, it is not feasible to utilize full knowledge of impostors because they can be unseen in biometrics.  
One related approach to ours (FSAD) is weakly-supervised anomaly detection. It aims to detect unseen impostors by utilizing labeled genuine and seen impostor data for the training. [PERSON] et al. explored this approach in touch-gesture authentication [31]. However, large-scale impostor data is necessary for weakly supervised models, which have the same constraint as the fully supervised approach.  
In this work, we study few-shot anomaly detection (FSAD), which addresses the drawbacks of previous approaches by leveraging small-scale seen impostor data.