Research on face recognition algorithm based on ELPP and fuzzy k-nearest neighbor algorithm

The essence of locality preserving projections (LPP) is a classic and linear approximation of Laplacian eigenmaps(LE), which can effectively extract the local manifold structure information. However, LPP is an unsupervised learning method, which cannot make good use of the category information of samples. Therefore, by introducing adjustable factors, the extended locality preserving projections (ELPP) method was formed considering sample categories while preserving local information. In addition, the performance of traditional KNN classifiers varies significantly due to the selection of value. However, the fuzzy k-nearest neighbor (FKNN) algorithm adds the fuzzy factor to make the generalization performance stronger. Therefore, this paper combines fuzzy K-nearest neighbor algorithm with ELPP to form FELPP learning model. Before using FELPP model, it is necessary to find out the optimal value of the tunable factor. Then the superiority of FELPP algorithm is proved by the test results on the Yale, ORL, and AR face database.