Palmprint verification using hierarchical decomposition

A reliable and robust personal verification approach using palmprint features is presented in this paper. The characteristics of the proposed approach are that no prior knowledge about the objects is necessary and the parameters can be set automatically. In our work, a flatbed scanner is adopted as an input device for capturing palmprint images; it has the advantages of working without palm inking or a docking device. In the proposed approach, two finger-webs are automatically selected as the datum points to define the region of interest (ROI) in the palmprint images. The hierarchical decomposition mechanism is applied to extract principal palmprint features inside the ROI, which includes directional and multi-resolution decompositions. The former extracts principal palmprint features from each ROI. The latter process the images with principal palmprint feature and extract the dominant points from the images at different resolutions. A total of 4800 palmprint images were collected from 160 persons to verify the validity of the proposed palmprint verification approach and the results are satisfactory with acceptable accuracy (FRR: 0.75% and FAR: 0.69%). Experimental results demonstrate that our proposed approach is feasible and effective in palmprint verification.