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.
palmprint recognition dual-tree complex wavelet shape of Gaussian Fisher Linear Discriminant
Meiru Mu Qiuqi Ruan Yue Ming
Institute of Information Science, Beijing Jiaotong University, Beijing, China
国际会议
2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)
北京
英文
1406-1409
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）