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A Spectral Domain Dominant Feature Extraction Algorithm for Palm-print Recognition
Hafiz Imtiaz, Shaikh Anowarul Fattah
Pages - 130 - 144 | Revised - 01-05-2011 | Published - 31-05-2011
Published in International Journal of Image Processing (IJIP)
MORE INFORMATION
KEYWORDS
Spectral Feature Extraction, Principal Component analysis (PCA), Two-Dimensional Fourier Transform, Classification, Palm-print Recognition, Entropy
ABSTRACT
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.
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Mr. Hafiz Imtiaz
Bangladesh University of Engineering and Technology - Bangladesh
Dr. Shaikh Anowarul Fattah
Bangladesh University of Engineering and Technology - Bangladesh
sfattah@princeton.edu
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