Fingerprint Recognition Implementation Using MATLAB

The fingerprint recognition process using MATLAB involves several key steps. First, fingerprint images are read using MATLAB's imread() function and undergo preprocessing operations including image enhancement through techniques like histogram equalization and noise removal using filters such as median or Wiener filters. Next, crucial fingerprint features are extracted, including ridge patterns and minutiae features (ridge endings and bifurcations) using image processing algorithms like Gabor filtering and morphological operations. The system then builds a fingerprint database where feature vectors of known fingerprints are stored using MATLAB's data structures like tables or arrays. During the recognition phase, input fingerprint features are compared with database entries using matching algorithms such as Euclidean distance calculation or correlation-based matching to find the optimal match. Finally, recognition results are generated based on matching scores, typically implemented through threshold comparison. This example provides comprehensive understanding of fingerprint recognition principles and implementation workflow, demonstrating practical applications of image processing and pattern recognition techniques in biometric systems.