Shape parameters of Gaussian as descriptor for palmprint recognition based on Dual-tree Complex Wavelet Transform

The multiscale and multidirectional transform is a tool that has been used widely in the last decade for image processing. This paper presents a novel image feature descriptor for palmprint recognition based on the Dual-tree Complex Wavelet transform (DT-CWT), which provides a local multiscale description of images with good directional selectivity, invariance to shifts, insensitive to illumination and in-plane rotations. Instead of exploiting the DT-CWT-derived coefficients directly, which are highly-dimension, we investigate a statistical model to characterize the image in the transform domain. It is experimentally founded that the DT-CWT-derived magnitude of one palmprint image approximates a lognormal distribution, i.e. the logarithmic transformation of DT-CWT-derived magnitude is close to a Gaussian model. Thus the shape parameters (mean and standard deviation) of Gaussian are exploited to construct the feature descriptor for palmprint recognition in this paper. This process brings computational efficiency. For capturing the spatial structure information, each image is partitioned into many quadtree-based subblocks, whose DT-CWT-derived magnitude destributions are similar to that of the whole image. Finally the Fisher Linear Discriminant (FLD) classifier is used for palmprint recognition. Experiments are carried out on the BJTU PalmprintDB (V1.0) of 3,460 images. The results demonstrate the high recognition performance of our proposed method.