In this paper

a discriminative locally linear embedding algorithm on image recognition

which considers spatial relationship of pixels and class information in order to improve the performance of locally linear embedding (LLE)

is presented. First

neighbor matrix

which is used to compute weight matrix

is constructed by adaptive image Euclidean distance

and features are reconstructed using the weight matrix. And then intrinsic lower-dimensional space of data is reconstructed. Finally

linear discriminant analysis is utilized to introduce class information to solve the defects that LLE can’t reconstruct test samples and classify. Experiments are carried on FRAV2D and ORL databases. Comparing our proposed algorithm with popular algorithms in face recognition

these results show that a discriminative LLE can keep the best manifold structure and class information

and improve the accuracy of face recognition.