Bayesian Theory-Based Fingerprint Recognition Algorithm and Learning Toolkit

In this article, we present an advanced fingerprint recognition algorithm and learning toolkit based on Bayesian theory. The algorithm leverages Bayesian probability theory to achieve accurate fingerprint identification, along with capabilities for feature code extraction and feature pair acquisition. Key implementation aspects include probabilistic modeling of fingerprint features using Bayesian classifiers, pattern recognition algorithms for feature point matching, and optimization techniques for probability calculations. This algorithm can be applied not only in fingerprint recognition systems but also plays significant roles in related fields such as security technology and identity verification. The accompanying learning toolkit provides comprehensive code examples demonstrating Bayesian probability calculations, feature extraction methods using image processing techniques, and matching algorithms that compare feature pairs against database templates. Through this toolkit, users can better understand and apply Bayesian theory principles and methodologies in fingerprint recognition, thereby enhancing both the accuracy and efficiency of fingerprint identification systems.