Palmprint Recognition Using 2D-FLDA From a Single Image Per Person

Abstract

Two dimensional Fisher linear discriminant analysis(2D-FLDA) is a very effective method for palmprint recognition. However, it cannot be used when each object has only one training sample because the within-class scatter matrices cannot be calculated. In this paper, a novel method is developed to solve this problem. Using the block segmentation, wavelet transform, and sampling methods, a new training set containing three training samples in each class can be obtained. Then the 2D-FLDA can be applied to extract the discriminant palmprint feature vectors. Finally the pattern classification can be implemented by the nearest neighbor classifier. Experimental results on the PolyU palmprint database show that the proposed method is efficient and it has better recognition performance than many existing schemes.