Research on fast identification technology of forged fingerprints based on the improved K-means algorithm

Abstract

In order to overcome the low accuracy of the traditional method, a fast identification method based on the improved K-mean algorithm is proposed. Spatial grid block model is constructed to extract the fingerprint texture features and then the fingerprint profile features are detected using the edge outline extraction method. The Kalman fusion method is used to reconstruct fingerprint information. Using the neighbourhood distributed retrieval method, fingerprint image feature fusion is realised and the texture feature extraction model for forged fingerprints is established. The K-means clustering method is used for fingerprint feature clustering to realise fast identification of forged fingerprints. Experimental results show that the identification accuracy of this method is higher than 0.85, and the identification stability is good. The signal-to-noise ratio of fingerprint images is always between 25.3 dB and 82.3 dB, and the imaging quality is high, indicating that this method can realise fast and accurate identification of forged fingerprints.