Biometric analysis for finger vein data: two-dimensional kernel principal component analysis

Abstract In this paper, a whole identification system is introduced for finger vein recognition. The proposed algorithm first maps the input data into kernel space, then; two-dimensional principal component analysis (2DPCA) is applied to extract the most valuable features from the mapped data. Finally, Euclidian distance classifies the features and the final decision is made. Because of the natural shape of human fingers, the image matrixes are not square, which makes them possible to use kernel mappings in two different ways—along row or column directions. Although some research has been done on the row and column direction through 2DPCA, our argument is how to map the input data in different directions and get a square matrix out of it to be analyzed by 2DPCA. In this research, we have explored this area in details and obtained the most significant way of mapping finger vein data which results in consuming the least time and achieving the highest accuracy for finger vein identification system. The authenticity of the results and the relationship between the finger vein data and our contribution are also discussed and explained. Furthermore, extensive experiments were conducted to prove the merit of the proposed system.