Pyramid Histogram of Double Competitive Pattern for Finger Vein Recognition

Finger vein is a new and secure biometric for personal authentication due to its line-structure network with abundant local and orientation features. However, these features cannot be well represented by the existing local descriptors. To effectively utilize these rich orientation features in finger vein images, this paper proposes a new local descriptor, namely, pyramid histogram of double competitive pattern (PHDCP). For a finger vein image, the PHDCP first obtains a bank of filtered images using Gabor filters with large kernel size and rich orientations, by which the local line features are captured. Then, the orientation orders with the largest and smallest responses, which are the most robust features, are selected to generate an encoded map. Finally, a column-partition-based pyramid histogram extraction method is presented to capture the hierarchical features from the encoded image. Numerical experiments are conducted on two public finger vein data sets, MMCBNU_6000 and UTFVP. The experimental results demonstrate that the proposed PHDCP performs much better than the existing local descriptors.