Revolutionizing Fingerprint Forensics: Regeneration and Gender Prediction with Gabor Filters, Otsu's Technique, and Deep Learning

This research study presents a novel approach for fingerprint regeneration and gender prediction using Gabor filters and Otsu's thresholding technique in combination with deep learning. The proposed method is designed to regenerate damaged or distorted fingerprints by enhancing their quality through the application of Gabor filters and Otsu's thresholding technique. Once the fingerprints are regenerated, they are fed into a deep learning model for gender prediction. The model is trained on a large dataset of fingerprint images, which is also modified in categories to accurately predict the gender of an individual based on their fingerprints. The proposed approach achieves high accuracy rates for both fingerprint regeneration and gender prediction, making it a promising tool for forensic analysis and identification.