Finger vein recognition algorithm based on improved weighted sparse representation

Abstract

For single-features based on finger vein image texture features (LBP), image gradient features (HOG), and other features with poor image contrast, the feature extraction effect is not ideal, and the sparse representation classification method based on residual size classification is used in low-quality images. This paper proposes a method for finger vein recognition based on improved weighted sparse representation. In order to reduce the heterogeneity of the samples participating in sparse reconstruction, this paper weights each training sample based on the sum of existing sparse coefficients as a classification basis to reduce the heterogeneity of samples participating in sparse reconstruction. On this basis, this paper uses the sum of intra-class coefficients instead of reconstruction errors as the basis for classification judgment. It has a high recognition rate for finger vein images with poor contrast.