# CoreTemp: Coreset Sampled Templates for Multimodal Mobile Biometrics
## 1 Introduction  
Smart devices, such as smartphones and tablets, play a key role in everyday life. Despite the technological advancements that have enabled such devices to dominate our lives, even the latest security measures are becoming outdated and expose users to various threats. For example, personal identification numbers, passwords, or patterns, while providing some degree of security, are especially prone to shoulder-surfing. Whereas biometrics-based methods such as fingerprint [1], facial [2], or iris recognition [3] do provide more security, most are limited to authentication at the point of entry. Unlike physical biometrics, which primarily authenticate, i.e., one-to-one matching, at the point of entry, continuous authentication involves ongoing verification. Behavioral biometrics facilitates a seamless and smooth authentication process in this regard. This body of literature involves keystrokes [4], gait features [5], and touchstroke dynamics [6]. However, behavioral biometrics inherently includes various characteristics within a single identity, varying on factors such as mood. Therefore, the machine learning approach has been widely adopted, especially for touchstroke dynamics [6; 7; 8; 9]. For clarity, we note that touchstroke dynamics are coupled with gait features, whereas, in this work, we refer to touchstroke-gait feature multimodal signals as touchstrokes for brevity. The touchstroke authentication problems can be framed as traditional binary classification (closed-set) [6; 7; 8; 10; 11; 12; 13; 14], anomaly detection (closed-set) [7; 15], or open-set authentication [16] problems. Among the most primitive is traditional binary classification, where access to an abundant amounts of genuine and impostor data is assumed, and the model is trained as a binary classifier upon such data. However, [7; 15] points out that the accessibility to impostor data is unrealistic, thus reformulating the problem as anomaly detection, where only genuine data are taken to train an anomaly detector. Unfortunately, anomaly detectors are widely known to suffer from the problem of feature representation collapse [17]. Moreover, though arguing as anomaly detectors, most rely upon limited generated or reinforced impostor data [7; 13; 14; 18; 19; 20; 21]. Also, training the model individually on genuine user data can be resource-consuming, make further updates and maintenance inefficient, and prone to model parameter breaches. To resolve the problems above [16], introduces an open-set authentication (OSA) for touchstroke biometrics, where the model is trained upon a multiple identity pretraining dataset. Upon distribution of the model, the enrollment phase involves a simple extraction of the touchstroke features from the user inputs, where the extracted features are stored as the template, in contrast to the training of models in binary classification or anomaly-detection frameworks. Subsequently, in the authentication phase, the input is likewise forwarded through the model, where its features are compared to measure the nearest neighbor distance with the stored template. By nature, OSA removes the shortcomings of binary classification of anomaly detection, enabling a more secure and efficient authentication. Specifically, its application with transformer-based architectures [22; 23], regarding the temporal nature of touchstroke dynamics, showed robust performance. State-of-the-art was achieved by a model called PIEformer [16], attempting to resemble the effect of ensembles of multiple Transformers while leading to minimal increment in model parameters and computational complexity. While OSA with PIEformer sets a notable benchmark, we make the following observations. 1. The touchstroke input from the user may be substantial in reality, leading to a bulky template where resource-limited mobile devices may hinder finding the nearest neighbor distance. Hence, a method of extracting a user-representative, robust subset of the original template should be investigated. 2.