Feature Extraction and Recognition of Fingerprint Using KNN-SIFT Algorithm

The biometric identification method of fingerprint recognition is quite popular because of its uniqueness and durability. The KNN-S 1FT algorithm is used in this research work to present a novel method for extracting and recognizing fingerprint features. The extraction of fingerprint feature information and fingerprint matching are the two main topics of the article. For the purpose of handling image differences brought on by scale, rotation, and affine transformations, a number of fingerprint feature extraction techniques are explored, with a focus on the SIFT algorithm. The KNN-S 1FT technique boosts the precision and dependability of fingerprint matching by combining the power of machine learning with pattern recognition. Experimental tests on a benchmark fingerprint database show that the proposed algorithm outperforms other cutting-edge methods in terms of accuracy and reliability. The findings of this study have broad ramifications for fields requiring safe identification procedures, including forensic investigations, access control systems, and law enforcement. The KNN-S 1FT technique that has been suggested helps to create sophisticated, effective fingerprint identification systems.