Anomaly user detection is an essential system for automating user addition in user recognition systems. There are two ways to add users in a user recognition system: manually collecting user data and retraining the model [
1‐
4] or determining whether the input data is from a new user or an existing user and retraining the model accordingly [
5‐
8]. The former method can achieve high accuracy because a supervisor recognizes new users, collects high-quality data, and retrains the existing model, but it requires many steps because a supervisor must do it manually [
9,
10]. Therefore, various methods using anomaly detection to classify data into existing users and new users and retrain the model have been researched recently, despite the disadvantage of having slightly lower accuracy than the former method [
7,
8,
11,
12]. The existing anomaly detection methods can be classified into machine learning algorithm-based methods [
13‐
16] and deep learning algorithm-based methods [
17‐
22]. Each algorithm is further divided into supervised learning [
23,
24], semi-supervised learning [
25,
26], and unsupervised learning [
27,
28]. Among various techniques, semi-supervised learning methods are gaining considerable attention in research [
15,
16,
18,
25,
26]. These methods utilize only existing user data to establish a discriminative boundary. This boundary is then tightened to identify any data outside of it as abnormal. Therefore, as the models currently under investigation are trained exclusively on the data of existing users, new users data that exhibits high similarity to the training data is likely to be mistakenly classified as belonging to an existing user [
29,
30]. Additionally, existing anomaly detection models based on semi-supervised learning require high-performance servers due to their large model size, making a system capable of transmitting data to the server also necessary [
31]. Therefore, there is a real-time performance degradation [
32,
33] caused by the overhead of transmitting user data from the edge node to the server after measurement. Consequently, this paper proposes a system that directly recognizes new users on the edge node, rather than on the server, in order to achieve real-time detection of new users. This system involves measuring data and incorporates a semi-supervised learning-based anomaly detection model that relies solely on existing user data to determine new users. This paper proposes a system that detects anomaly data to identify new users in a user recognition system. Unlike sending data to a dedicated server for training, the system runs on an STM32F207ZG MCU-based edge node for real-time inference. The edge node includes a system [
34] for measuring and generalizing users’ foot pressure data. This paper proposes a system that augments the existing system by incorporating a model for classifying new users using multiple images and the LeNet-5 model [
35‐
38], which is a CNN algorithm known for its low number of weights and high accuracy in image recognition. To determine a new user with multiple images, the captured images were inputted into the user recognition model, and the mean of all predicted values and the threshold value were compared. The model was trained using existing user data because it cannot have advance knowledge of new user data. So, it was trained based on the Semi-Supervised Learning approach to detect abnormal data. For the experiments, datasets with different levels of similarity were used to compare their accuracy. High similarity datasets, such as the foot pressure dataset, and low similarity datasets, such as Fashion-MNIST and Digit-MNIST, were utilized. As a result, the existing Anomaly Detection models exhibited an average accuracy of 83% on low similarity datasets. However, on the high similarity foot pressure dataset, there was a decrease of approximately 22%, resulting in an average accuracy of 61%. To overcome this issue, the model size needs to be adjusted, but this is limited by the edge node’s computing resources. However, the model proposed in this paper demonstrated an accuracy increase of approximately 29% compared to existing Anomaly Detection models when using datasets with high similarity. It achieved an accuracy of 89%. Additionally, when Quantization and Pruning [
39] were performed for accuracy measurement on the Edge node, there was a slight decrease in accuracy by approximately 3%, resulting in an accuracy of 86% for new user classification. Additionally, this model utilizes the LeNet-5 model [
35‐
38] on the edge node, enabling real-time recognition of both existing and new users. Consequently, it is possible to utilize the model in various systems, such as a system that automatically adds new users, by transmitting only abnormal data to the server for transfer learning and receiving the model from the server. The main achievements of this paper can be summarized as follows.
1. The proposed system is capable of accurately recognizing new users based on similar datasets with high similarity.
2. The system can recognize new users in real-time with high accuracy, using limited computing resources on the edge node.