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

In this paper, a frequency domain feature extraction algorithm for palmprint 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 palmprint 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.