MATLAB Fingerprint Recognition System

A fingerprint recognition system is a biometric identification technology that verifies or identifies individuals by analyzing detailed features in fingerprint images, such as ridges, bifurcations, and endpoints. Implementing this system in MATLAB 7.0 primarily involves the following steps:

Image Preprocessing Raw fingerprint images often contain noise and blurred regions. Quality improvement requires operations like filtering, binarization, and thinning. Common preprocessing techniques include Gaussian filtering for noise removal, adaptive threshold binarization, and morphological processing to optimize ridge structures. In MATLAB implementation, functions like imgaussfilt() and imbinarize() with adaptive thresholding can be used, followed by morphological operations such as bwmorph() for thinning.

Feature Extraction Key fingerprint features include minutiae points (e.g., bifurcations and endpoints) and global features (e.g., core and delta points). MATLAB's edge detection algorithms (e.g., edge() function with Canny method) and thinning algorithms help locate these feature points. Subsequent feature encoding can be performed using algorithms like orientation field analysis, implemented through gradient computation and directional filtering.

Feature Matching Extracted features are compared against templates in the database. Common matching approaches include point pattern matching (using similarity scoring) or transformation-based matching (involving rotation and translation correction). MATLAB's built-in matrix operations and distance calculation functions (e.g., pdist2() for Euclidean distance) efficiently implement this step through feature vector comparison and score calculation.

Result Validation The system outputs matching scores or confidence levels to determine whether the fingerprint matches the target template. Performance optimization involves adjusting thresholds to balance the False Acceptance Rate (FAR) and False Rejection Rate (FRR). This can be implemented using ROC curve analysis and threshold optimization algorithms in MATLAB.

The MATLAB implementation of this system fully utilizes the Image Processing Toolbox capabilities, making it suitable as a starting point for biometric recognition research. Developers can further extend the algorithms, such as incorporating deep learning approaches to improve recognition accuracy in complex scenarios using MATLAB's Deep Learning Toolbox.