Application of improved stacking ensemble learning in intelligent terminal fingerprint recognition

In order to solve the problems of high RMSE value and error acceptance rate, as well as poor recognition performance in existing fingerprint recognition methods, the application of improved stacking ensemble learning in intelligent terminal fingerprint recognition was studied. Firstly, image quality is improved through greyscale processing, Gaussian filtering denoising, and Gabor filtering. Secondly, the Sobel operator is used to calculate the gradient direction and divide it into blocks to extract fine node features, which are described by curvature to support fingerprint matching. Finally, the improved algorithm is used for fingerprint recognition, which is trained and predicted by the base learner. The output is then subjected to feature enhancement, weighted fusion, and second layer learner training to obtain the final fingerprint recognition result. The experimental results show that the RMSE value and error acceptance rate of the proposed method are low, and the identification effect is good.