Exploring Opencv for Digital Fingerprint Iden...

Exploring Opencv for Digital Fingerprint Identification in Forensics Analysis

Author Name : Prof. A. P. Mohad , Abhishek Tete , Aniruddha Deotale , Darshil Sukhadiya , Dilip Thakur

ABSTRACT Fingerprint detection is a critical task in biometric identification systems and forensic analysis. In this implementation, we present a Python-based solution using the minutiae-based approach to extract and visualize fingerprint characteristics. The process consists of image preprocessing, binarization, ridge segmentation, and minutiae extraction. The Python implementation utilizes the OpenCV library for image processing and NumPy for numerical computations. Image preprocessing techniques, such as histogram equalization and filtering, are employed to enhance the fingerprint image and improve ridge clarity. Subsequently, Otsu's method is used for automatic image binarization, converting the image into a binary format for easy processing. The implementation then visualizes the detected minutiae points by marking them as red (ridge endings) and green (bifurcations) on the original fingerprint image. Additionally, a line indicating the minutia direction is drawn to enhance the visualization. Fingerprint recognition remains a vast and evolving field, and researchers and practitioners should explore cutting-edge methods to achieve higher accuracy and efficiency.