<![CDATA[In this study, fingerprint identification and classification of low-quality fingerprints have been analyzed accordingly. As technology advances and methodologies evolve, staying at the forefront of research and innovation is imperative. The challenges addressed in this paper provide a foundation for future investigations and underscore the importance of developing resilient and adaptable biometric systems for real-world applications. The quest for accurate, efficient, and robust fingerprint identification in adverse conditions is a testament to the continuous evolution and refinement of machine learning and deep learning approaches in biometrics. While deep learning models exhibited improved performance, it is essential to acknowledge the need for further research and development in this domain. Additionally, integrating multimodal biometric systems and combining fingerprint data with other biometric modalities might present a viable avenue for mitigating the limitations associated with degraded fingerprints. In this paper, we develop a fingerprint identification approach for low-quality fingerprint images. The success rate accuracy of the propped algorithm for the low-quality fingerprint images should be significantly better than that of the standard local minutia approach. The main design of our deep learning approach is based on detecting and extracting the primary correlation during the training and using the correlation feature map to calculate the distance between the low-quality fingerprint images during the predicting phase. The experimental results show a very promising repulsing and high prediction accuracy.]]>
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