Abstract
We propose an efficient method for principal line extraction from the palmprint and geometric framework for analyzing their shapes. This representation, along with the elastic Riemannian metric, seems natural for measuring principal line deformations and is robust to challenges such as orientation variation and re-parameterization due to pose variation and missing part, respectively. The palmprint texture is investigated using the fractal analysis; thus the resulting features are fused with the principal line features. This framework is shown to be promising from both - empirical and theoretical - perspectives. In terms of empirical evaluation, our results match or improve the state-of-the-art methods on three prominent palmprint datasets: PolyU, CASIA, and IIT-Delhi, each posing a different type of challenge. From a theoretical perspective, this framework allows fusing texture analysis and shape analysis.