MATLAB Fingerprint Recognition: Implementation and Algorithms

In fingerprint recognition technology, MATLAB serves as a widely adopted programming language and tool that finds extensive application in fingerprint image processing. By implementing key steps such as fingerprint image preprocessing, feature extraction, and feature matching within the MATLAB environment, one can achieve efficient and accurate fingerprint recognition systems. The preprocessing phase typically involves using MATLAB's image processing toolbox functions like `imgaussfilt()` for Gaussian filtering to reduce noise, `histeq()` for contrast enhancement, and `imbinarize()` for image segmentation. These operations help improve image quality and prepare the data for subsequent feature extraction. For feature extraction, algorithms can be implemented using MATLAB's built-in functions such as `bwskel()` for skeletonization and custom code for minutiae point detection (ridge endings and bifurcations). The feature matching process often employs correlation-based techniques or minutiae matching algorithms, which can be optimized using MATLAB's matrix operations for rapid comparison. MATLAB provides a comprehensive set of image processing functions and algorithms that facilitate various fingerprint image manipulations including filtering, enhancement, and segmentation. These capabilities significantly improve the accuracy and reliability of fingerprint recognition systems. Furthermore, MATLAB's visualization tools like `imshow()` and `plot()` enable developers to validate each processing stage effectively. In summary, MATLAB's application in fingerprint recognition offers substantial convenience and development potential for both research and practical implementations of fingerprint recognition technology, supported by its robust algorithmic capabilities and efficient programming environment.