A novel pre-processing technique for frequency domain palm-print recognition

Abstract EN

In this paper, a novel pre-processing algorithm is introduced to identify the principal lines from a palm-print image and a frequency domain feature extraction algorithm is then employed for palm-print recognition, which can efficiently capture the spatial variations in the principal lines of a palm-print image.

The entire image is segmented into several small spatial modules.

The task of feature extraction is carried out in local zones using two dimensional discrete Fourier transforms (2D-DFT).

The proposed dominant spectral feature selection algorithm offers an advantage of very low feature dimension and it is capable of capturing precisely the detail variations within the palm-print image.

It is shown that because of the pre-processing step, the discriminating capabilities of the proposed features are enhanced, which results in a very high within-class compactness and between-class reparability 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.