Fast Fingerprint Recognition Algorithm Using Haar Wavelet Transform

Design and implementation of a fast fingerprint recognition algorithm utilizing Haar wavelet transform. In this algorithm, we employ Haar wavelet transformation as the feature extraction method, performing multi-level wavelet decomposition on fingerprint images to obtain frequency-domain features at different scales. These extracted features are then used to construct a fingerprint recognition model, where matching is achieved by comparing target fingerprints against stored templates in the database. The implementation involves key functions such as wavelet decomposition using filter banks, feature vector normalization, and similarity measurement through Euclidean distance or correlation coefficients. Additionally, we optimized the algorithm by implementing pyramidal processing for computational efficiency and incorporating threshold-based matching acceleration techniques. Experimental validation demonstrates that our algorithm achieves strong performance in fingerprint recognition tasks with improved accuracy and processing speed.