期刊名称：International Journal of Image Processing (IJIP)
电子版ISSN：1985-2304
出版年度：2011
卷号：5
期号：2
页码：130-144
出版社：Computer Science Journals
摘要：In this paper, a spectral feature extraction algorithm is proposed for palm-print recognition, which can efficiently capture the detail spatial variations in a palm-print image. The entire image is segmented into several spatial modules and the task of feature extraction is carried out using two dimensional Fourier transform within those spatial modules. A dominant spectral feature selection algorithm is proposed, which offers an advantage of very low feature dimension and results in a very high within-class compactness and between-class separability of the extracted features. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palm-print databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.
