Palmprint recognition using Gabor-based local invariant features

Variations occurred on palmprint images degrade the performance of recognition. In this paper, we propose a novel approach to extract local invariant features using Gabor function, to handle the variations of rotation, translation and illumination, raised by the capturing device and the palm structure. The local invariant features can be obtained by dividing a Gabor filtered image into two-layered partitions and then calculating the differences of variance between each lower-layer sub-block and its resided upper-layer block (called local relative variance). The extracted features only reflect relations between local sub-blocks and its resided upper-layer block, so that the global disturbance occurred on palmprint images is counteracted. The effectiveness of the proposed method is demonstrated by the experimental results.