A Novel Principal Component Analysis Neural Network Algorithm for Fingerprint Recognition in Online Examination System

To solve the authentication problem in online examination system for large-scale, a novel principal component analysis neural network algorithm for fingerprint recognition is presented. Based on the introduction of the basic principles of feature selection and feature extraction for principal component analysisiquestConstruction of Symmetric subspace model based on principal component analysis neural network, and the convergence of Symmetric subspace algorithm is analyzed.The feasibility of using this algorithm for fingerprint recognition problems is also discussed. Algorithm to meet the convergence conditions and to simplify the complex pre-processing steps, greatly reducing the computational complexity, improve the speed of the identification. Experimental results indicate that the algorithm can obtain a higher recognition rate compared with BP neural network recognition algorithm and this new algorithm presents a feasible and effective way to research on fingerprint recognition algorithm for the examination.