Abstract: In order to improve the robustness and accuracy of the feature representation of 3D
palmprint, a method integrating the geometric and directional features of curved surfaces was
proposed. Based on the existing method using the surface type (ST)-based coding to extract geometric
features of a 3D palm, we proposed to use the shape index (SI)-based coding to jointly characterize
the geometric features of 3D palmprints. This operation can effectively reduce the impact on accuracy
brought by the error encoding caused by the threshold. Moreover, we proposed a multi-scale modified
competitive coding (MSMCC) to characterize the orientation features. The multi-dictionary
collaborative-representation (CR)-based framework was employed to merge the geometric and
orientation features into the decision level to perform identification. Extensive experiments on the
public 3D palmprint database prove that the proposed method can achieve an optimal rank-1
recognition accuracy while maintaining a relatively low computational complexity.

Key words: 3D palmprint recognition, biometrics, shape index coding, modified competitive coding;
collaborative representation