Palm-dorsa vein recognition based on Two-Dimensional Fisher Linear Discriminant

In Fisher Linear Discriminant (FLD), the within-class scatter matrix is always singular. To overcome the above problem and preserve discriminatory information, a new method for palm-dorsa vein feature extraction based on Two-Dimensional FLD (2DFLD) is presented in this paper. We applied PCA, PCA+FLD and 2DFLD to extract the palm-dorsa vein feature subspace. The images to be recognized were projected onto the low-dimensional subspace. A classifier to vein matching based on cosine distance was used. Experimental results suggested that the recognition rate of PCA+FLD is about 4.84% higher than that of PCA. Compared with PCA+FLD, 2DFLD is able to yield recognition rate as high as 98.44%, with accuracy enhanced by 7.51%, while the feature extraction time is only 0.4 s. It was demonstrated that the algorithm is effective and quick.