Abstract:
The integration of deep learning technologies, particularly Convolutional Neural Networks (CNNs), has
profoundly transformed fingerprint identification, providing a more effective and accurate approach compared to
traditional methods. These deep learning models, trained on extensive datasets of fingerprint images, excel in extracting
intricate patterns and unique features essential for precise fingerprint matching, even amidst challenging conditions like
varying image quality, orientation, or lighting. Notably, the adaptability of deep learning-based systems, continuously
refining their accuracy and performance with additional data and fine-tuning, proves indispensable in dynamic
environments with ongoing fingerprint data collection. Moreover, the convergence of deep learning with other
biometric modalities, such as facial recognition or iris scanning, has led to the development of robust multimodal
biometric systems, enhancing security through layered verification mechanisms. However, persisting challenges, such
as the acquisition of large annotated datasets and the mitigation of bias in training data, underscore the importance of
addressing these issues to further enhance the reliability and performance of deep learning-based fingerprint
identification systems. This survey paper aims to comprehensively review and analyze the application of deep learning
techniques in fingerprint identification systems, providing valuable insights into current advancements, challenges, and
future directions in the field, thereby serving as a resource for researchers, practitioners, and enthusiasts seeking a
nuanced understanding of this critical area in biometrics.