Miniature and Orientation Map Feature Extraction

Abstract

Biometric identification involves using a person's unique physical characteristics, such as fingerprints, face recognition or iris, to identify them. Fingerprint recognition is the most used biometric method, as each person's fingerprints are unique. In this process, features are extracted from fingerprint images based on the ridges and edges present in the images. Supervised learning techniques are then used to recognize the palm print images. The features are then recognized using distance metrics. Fingerprint recognition is efficient compared to other methods, and its performance can be measured using metrics such as true positives, true negatives, false positives, false negatives, sensitivity, specificity, and accuracy. The Minutiae and Orientation map features are also extracted from fingerprint images to aid identification. The Minutiae feature extraction helps identify significant ridges and corners, while the Orientation map features texture-based information. Matching fingerprints is based on measuring the distance between the extracted features. Accuracy, Specificity, and Sensitivity are used to gauge the process' performance.