Detecting unregistered users through semi-supervised anomaly detection with similarity datasets

Abstract

Recent research has focused on exploring systems that incorporate anomaly detection models to automate the addition of users in user recognition systems. Anomaly detection, a method used to distinguish between new and existing users by identifying abnormal images, has gained significant attention. Researchers have been actively investigating the Semi-Supervised Learning method, which utilizes only existing user data to differentiate between existing and new users. However, existing semi-supervised learning based anomaly detection models exhibit high performance on datasets with low similarity but experience a sharp decline in performance on datasets with high similarity. Furthermore, their large model size makes it challenging to execute them on edge nodes. To address these limitations, this paper proposes a model that can be executed on edge nodes and guarantees good performance on both low and high similarity datasets. The proposed model utilizes the LeNet-5, a user recognition model with fewer weights and multiple images as input, for classifying new users. This study compared the existing anomaly detection models with the proposed model using three datasets with varying similarities. The performance evaluation involved comparing the accuracy, ROC curve, and AUC of each model on a training server. Subsequently, the top three models were optimized for execution on the edge node (STM32F207ZG MCU) and further evaluated by comparing their accuracy, inference speed, and model size. The results revealed that the proposed model achieved an approximate 53% improvement in accuracy compared to the existing anomaly detection models. Furthermore, when executed on the edge node, the proposed model demonstrated significant memory savings, with a maximum reduction of approximately 530% and approximately 40% reduction in flash memory usage compared to the existing models.