A Novel Deep Learning Framework for Fingerprint Image Enhancement and Accurate Recognition

This study presents a novel hybrid approach to fingerprint recognition, combining frequency domain filtering and deep learning techniques to enhance fingerprint quality and improve recognition accuracy. The proposed method first applies frequency domain enhancement to reduce background noise and clarify essential fingerprint features such as ridges and valleys. Subsequently, a convolutional neural network (CNN) is employed to further refine and correct the binary fingerprint image, addressing any residual misrepresentations. This approach leverages the strengths of both traditional image processing and modern deep learning, resulting in a more accurate and reliable fingerprint recognition system, particularly in noisy or distorted conditions. Experimental results demonstrate the effectiveness of this method in improving fingerprint image quality and recognition performance, making it a valuable solution for biometric security applications.