Fingerprint Recognition System Implementation Using MATLAB

In this project, we will develop a fingerprint recognition system using MATLAB programming language. Fingerprint recognition is an identification technology that determines individual identity by analyzing fingerprint patterns. This technology has gained widespread application in security systems, criminal investigations, and personnel management. Our program will implement various image processing algorithms and machine learning models, including Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs), for fingerprint identification and matching. Key implementation aspects include: - Preprocessing algorithms: Implementation of image enhancement techniques like Gabor filtering and histogram equalization to improve fingerprint ridge clarity - Feature extraction methods: Code for minutiae point detection (ridge endings and bifurcations) using morphological operations and orientation field calculation - Machine learning integration: MATLAB's Deep Learning Toolbox for CNN architecture design and Statistics and Machine Learning Toolbox for SVM classification - Matching algorithms: Development of similarity scoring systems based on extracted minutiae patterns We will also incorporate support for multiple fingerprint image input sources, including optical scanners and smartphone cameras, with appropriate image calibration and quality assessment routines. The implementation will include adaptive thresholding techniques to handle varying image quality from different devices. Through this project, we aim to conduct in-depth research on fingerprint recognition technology, enhance MATLAB programming capabilities in computer vision applications, and establish a foundation for future work in related fields. The code structure will emphasize modular design, allowing easy integration of additional algorithms and scalability for larger fingerprint databases.