Adapting a Previously Proposed Open-Set Recognition Method for Time-Series Data: A Biometric User Identification Case Study

Conventional classifiers are generally unable to identify samples from classes absent during the model’s training. However, such samples frequently emerge in real-world scenarios, necessitating the extension of classifier capabilities. Open-Set Recognition (OSR) models are designed to address this challenge. Previously, we developed a robust OSR method that employs generated—“fake”—features to model the space of unknown classes encountered during deployment. Like most OSR models, this method was initially designed for image datasets. However, it is essential to extend OSR techniques to other data types, given their widespread use in practice. In this work, we adapt our model to time-series data while preserving its core efficiency advantage. Thanks to the model’s modular design, only the feature extraction component required modification. We implemented three approaches: a one-dimensional convolutional network for accurate representation, a lightweight method based on predefined statistical features, and a frequency-domain neural network. Further, we evaluated combinations of these methods. Experiments on a biometric time-series dataset, used here as a case study, demonstrate that our model achieves excellent open-set detection and closed-set accuracy. Combining feature extraction strategies yields the best performance, while individual methods offer flexibility: CNNs deliver high accuracy, whereas handcrafted features enable resource-efficient deployment. This adaptability makes the proposed framework suitable for scenarios with varying computational constraints.