Feature-level fusion of palmprint and palm vein base on canonical correlation analysis

Canonical Correlation Analysis (CCA) is a standard tool in statistical analysis that measures the linear relationship between two data sets. In this paper, a multi-biometric approach which combines palmprint feature and palm vein feature based on Canonical Correlation Analysis is proposed. First, using a series of pre-processing and extracted the ROI (Region of interest) which has enhanced of the palmprint and palm vein images. Then, using local binary pattern (LBP) to extract the palm print and palm vein feature. Next, these two features are fused by CCA to form a combined feature which is applied to denote the identity of a person. This method makes it possible to fuse these features mentioned above together and decrease the dimension of the fusion feature. The results of experiments conducted on a database of 100 hands show that the CCA-based feature level fusion method has good performance.