Palmprint Recognition Based on Discriminative Local Binary Patterns Statistic Feature

In on-line palmprint recognition tasks, texture based feature extraction methods are widely adopted for palmprint representation, owing to their high performance. Gabor filter bank is among the most promising texture information extraction tool because of its multi-scale and multi-orientational characteristics. However, it is both time and memory intensive to convolve palm images with a bank of filters to extract features. In this paper, a novel palmprint texture representation is proposed, discriminative local binary patterns statistic (DLBPS), which is extracted for palmprint recognition. In this approach, a palmprint is firstly divided into non-overlapping and equal-sized regions, which are then labeled into Local Binary Patterns (LBP) independently. By calculating these patterns' distribution, the statistic features of the palmprint texture are attained. Subsequently, the Discriminative Common Vectors (DCV) algorithm is applied for dimensionality reduction of the feature space and solution of the optional discriminative common vectors. Finally, Euclidean distance and the nearest neighbor classifier are used for palmprint classification. Our experimental results demonstrate the effectiveness of the proposed DLBPS palmprint representation, which brings both high recognition accuracy rate and high speed benefits.