A survey of Fingerprint Identification System Using Deep Learning

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.