A method of recognition based on the feature layer fusion of palmprint and hand vein

In this paper, a method of recognition of multi-modal biometrics for palmprint and hand vein based on the feature layer fusion is proposed, combined with the characteristics of an improved canonical correlation analysis (CCA) and two dimensional principal component analysis (2DPCA). After pretreatment respectively, feature vectors of palmprint and hand vein images are extracted using two dimensional principal component analysis (2DPCA), then fused in the feature level using the improved canonical correlation analysis(CCA), so identification can be done by a adjacent classifier finally. Using this method, two biometric information can be fused and the redundancy of information between features can effectively eliminated, the problem of the high-dimensional and small sample size can be overcome too. Simulation experimental results show that the proposed method in this paper can effectively improve the recognition rate of identification.