Unconstrained Face Recognition Based on Weighted Discriminant Sparsity Preserving Embedding

The face images acquired in the unconstrained environment are complex and changeable, and sparse preserving projection (SPP) algorithm is not ideal for dimensionality reduction. In view of this problem, a weighted discriminant sparse preserving projection for unconstrained face recognition is proposed. Firstly, in order to enhance the reconstruction relationship between query sample and training samples of the same type, and class label information of samples and the in-class compact item are added. Secondly, due to the uneven quality of the sample under non-control environment, the sample distance weight is used to constrain the sparse reconstruction coefficient, which reduces the influence of similar singular samples and further improves the accuracy of the reconstruction relationship. Finally, the global constraint factor is added in the low-dimensional projection process, and the low-dimensional subspace distribution is made more compact and more discriminative by using the implicit identification information in the global distribution of the sample. the effectiveness of the method is verified by some experiments on the AR, the Extended Yale B, the LFW and the PubFig databases.