A DCT-Based Local Dominant Feature Extraction Algorithm for Palm-Print Recognition

摘要

In this paper, a frequency domain feature extraction algorithm for palm-print recognition is proposed, which efficiently exploits the local spatial variations in a palm-print image. The entire image is segmented into several small spatial modules and the effect of modularization in terms of the entropy content of the palm-print images has been investigated. A palm-print recognition scheme is developed based on extracting dominant spectral features from each of these local modules using a two-dimensional discrete cosine transform (2D-DCT). The proposed dominant spectral feature selection algorithm offers the advantage of having very low feature dimension, and it is capable of capturing precisely the variations in detail within the palm-print image. It is shown that because of modularization of the palm-print image, the discriminating capabilities of the proposed features are enhanced, which 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.