| dc.description.abstract | This study addresses the challenges of data scarcity and the diverse application scenarios in the field of fingerprint recognition by proposing a fingerprint recognition method based on self-supervised pretraining and a Siamese network architecture. This method aims to improve the accuracy and robustness of the model. As an essential technology for identity authentication, fingerprint recognition has been widely applied in payment systems, security management, and personal devices. However, the limited availability of public fingerprint datasets, which are predominantly standardized samples, fails to adequately cover the complex scenarios encountered in real-world applications, such as partial fingerprint degradation, noise interference, and cross-sensor variations. Consequently, the performance of models in practical applications is restricted. Moreover, the privacy and confidentiality of proprietary datasets further limit access to training data, representing a critical bottleneck in current fingerprint recognition research.
To address these challenges, this study first employs self-supervised learning techniques to fully explore the latent features in unlabeled fingerprint data through contrastive learning and pretraining. This approach provides a robust initialization for the model and reduces dependency on labeled data. Subsequently, a Siamese network architecture is designed, where two pretrained subnetworks with shared parameters are used to extract features from fingerprint images. The similarity between images is calculated using the Euclidean distance. This architecture effectively tackles challenging scenarios such as partial degradation, noise interference, and cross-sensor variations, thereby enhancing recognition performance.
The experimental results demonstrate that the proposed method achieves satisfactory recognition performance on fingerprint datasets. The pretrained model enables faster convergence during downstream fine-tuning, saving training resources and time. Furthermore, it exhibits high robustness and generalization capabilities when processing fingerprints in complex scenarios. | en_US |