Local Binary Pattern (LBP) is a powerful texture descriptor that is gray-scale and rotation invariant [3]. Because texture is one of the most clearly observable features in low-resolution palmprint images, we think local binary pattern based features are very discriminative for palmprint identification. In this paper, we propose a palmprint identification approach using boosted local binary pattern based classifiers. The palmprint area is scanned with a scalable subwindow from which local binary pattern histograms [4] are extracted to represent the local features of a palmprin image. The multi-class problem is transformed into a two-class one of intra- and extraclass by classifying every pair of palmprint images as intra-class or extra-class ones[19]. We use the AdaBoost[18] algorithm to select those sub-windows that are more discriminative for classification. Weak classifiers are constructed based on the Chi square distance between two corresponding local binary pattern histograms. Experiments on the UST-HK palmprint database show competitive performance.
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