# Few-Shot Continuous Authentication for Mobile-Based Biometrics
## 1 Introduction
### Motivation and Contribution  
Since mobile devices are personal and hardly shared, mobile-based biometric authentication can be deemed an anomaly detection problem, in which models differentiate a single genuine user and all other impostors (anomalies). Prior works have different assumptions about the availability of impostor data with respect to unsupervised, semi-supervised, and fully-supervised learning. The approach relying on unsupervised or semi-supervised anomaly detection assumes that impostor data cannot be obtained in advance, solely depending on unlabeled or genuine data for the training [2; 3; 4; 5; 6; 7]. However, this approach is poor at detecting impostors because they do not leverage prior knowledge of impostors. On the other hand, the fully-supervised approach assumes that genuine and all impostor profiles (seen impostors) are available for learning [3; 8; 9; 10; 16; 17]. An apparent issue of this approach is that models are not designed to work in an open-set setting--a practical scenario for biometrics, where it is unfeasible to have all impostor profiles available for training due to time and monetary costs. During authentication, the model may not infer correct decisions when encountering a never-seen impostor (unseen impostor). Considering the issues of previous approaches, we posit the mobile-based authentication as a few-shot anomaly detection (FSAD) problem [18]. FSAD is a subfield of weakly supervised learning in that only an incomplete set of anomaly classes is known at the training. In addition to the unavailability of complete knowledge of anomalies, FSAD is allowed to use only a few anomalous data. Recent works have addressed FSAD considering the availability of small-scale labeled anomaly data in real-world scenarios [19; 20; 21; 22]. Compared to the fully supervised approach, FSAD is practical in mobile-based authentication. First, our model is sample-efficient because it uses only a few _seen impostor_ (anomaly class) data for training. Secondly, our model aims to work in an open-set setting, where models encounter _unseen impostors_. By leveraging partial knowledge of seen impostors, our model excels at detecting unseen impostors more than unsupervised and semi-supervised models. This work is the first attempt to address mobile-based biometrics authentication from the FSAD vein. Specifically, we define the challenges as follows:  
1. Limited impostor profiles for training: The core challenge of FSAD is the limited availability of seen impostor profiles. However, the models need to be able to detect arbitrary unseen impostors during inference. 2. Class imbalance between genuine and impostor data: Since FSAD uses extensive genuine and small impostor data, models inherently suffer from the class-imbalance problem. This can emphasize the genuine class and under-fit the impostor classes, preventing learning the impostor patterns adequately. 3. Large intra-class variance of user behavior data: Due to the significant intra-class variance of raw touch sensor data, most touch-gesture authentication solutions have adopted hand-crafted features and conventional machine learning models such as support-vector machine, random forest, etc., which are more noise-robust than learned features [11; 12; 13; 14; 23; 24; 25; 26; 27; 28]. However, hand-crafted features lack refined feature information, making it hard to characterize delicate impostor patterns. To address the first two challenges, we propose random pattern mixing--a data augmentation method tailored for sequential touch sensor data. While data augmentation has been extensively studied for visual data [29], it is under-explored for anomaly detection [18] and sequential data [17]. A few primitive augmentation methods for sequential data, such as jittering and scaling [3; 16; 17], are intended to generate data of a minority class (seen impostors in our case) by adding small noises or changing their magnitudes slightly, for example.