Дослідження впливу фрактальної розмірності Хігучі в задачі біометричної верифікації користувача

Abstract

Biometric verification systems are pivotal for identifying individuals based on their physiological or behavioral characteristics, offering enhanced security over traditional authentication methods. Despite their advantages, the accuracy and reliability of biometric verification present challenges, necessitating innovative approaches for feature extraction and representation. This paper proposes integrating Higuchi’s Fractal Dimension (HFD) as an additional feature in autoencoder architectures to enhance feature extraction processes in biometric verification tasks. The incorporation of HFD, known for capturing signal complexity and self-similarity, is anticipated to improve the discriminative power of extracted features, thereby enhancing overall verification efficacy. The study focuses on biometric verification using sensor signals, aiming to examine and analyze the impact of adding additional features such as HFD on biometric verification results and evaluation metrics. In conclusion, the integration of Higuchi Fractal Dimension (HFD) features into autoencoder-based models for biometric verification tasks demonstrates a promising approach to enhancing the accuracy of biometric systems. This research confirms the hypothesis that additional signal information, provided by HFD features, substantially aids models in effectively distinguishing biometric patterns. However, computational costs associated with HFD calculations pose a challenge, particularly for applications requiring low latency. Future work should focus on developing optimized algorithms for HFD computations or exploring alternative methods to capture signal complexity with lower computational overheads. This study lays the groundwork for extended use of fractal dimensions in biometric verification, suggesting a fruitful direction for future research in improving the accuracy and efficiency of biometric systems through advanced signal processing methods.