Synthesis of DHV Images for the Enlargement of the Existing DataBase Based on PCA

It is very important for the performance evaluation of dorsa-hand vein (DHV) recognition algorithms to construct very large DHV databases. However, limited by the real conditions, there are no very large common DHV databases now. This paper introduces a novel synthesis method of DHV images using principal component analysis (PCA), which will be applied to enlarging the existing DHV image database to a large and new one. Based on the method of PCA, new data are synthesized with the feature extracted from the existing real data. The feature space grows up with the sample sets, the number of which is decided by our experiments. Extensive experiments show that the synthesized DHV databases can be very large. The experimental results show that the synthesized database has a good recognition rate, which indicates the proposed method performs well and would be applicable in the simulation test.