Fingerprint Matching Method Based on Ant Colony Algorithm

Based on previous research, this paper proposes a model and implementation of large-scale fingerprint image retrieval. In the retrieval, the C-means clustering method is used to quickly retrieve fingerprint images, and the shortest path algorithm is used to achieve effective matching fingerprints, and finally, the fingerprints with a higher matching rate are obtained, thereby realizing rapid retrieval and matching of fingerprint IDs. The matching fingerprint minutiae features are analyzed, a retrieval method that can be used for fast fingerprint retrieval is given, and the principle of the method is expounded. The algorithm is used to filter and remove the pseudo-feature points of all fingerprints and is used to calculate the similarity between all minutiae operators. Then the operator with the highest similarity is selected, and the corresponding minutiae pair is used as the registration point pair to complete the two minutiae registration transformation of the point set. And finally, the pairing relationship of minutiae points is established. After that, the time complexity and space complexity of the selected retrieval method is analyzed. All fingerprint pairs with the “identical” relationship provided in the data file are used as query images to verify the retrieval method in the fingerprint dataset. The shortcomings and deficiencies of this method are pointed out, and other ways to solve this problem are introduced.