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

Content Provider	Springer Nature Link
Author	Imtiaz, Hafiz Fattah, Shaikh Awarul
Copyright Year	2012
Abstract	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.
Starting Page	1179
Ending Page	1204
Page Count	26
File Format	PDF
ISSN	0278081X
Journal	Circuits, Systems, and Signal Processing
Volume Number	32
Issue Number	3
e-ISSN	15315878
Language	English
Publisher	SP Birkhäuser Verlag Boston
Publisher Date	2012-10-16
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Feature extraction Classification Discrete cosine transform Entropy-based information content Dominant spectral feature Palm-print recognition Modularization Circuits and Systems Electrical Engineering Signal, Image and Speech Processing Electronics and Microelectronics, Instrumentation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Signal Processing

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