A DCT-Based Local Dominant Feature Extraction Algorithm for Palm-Print Recognition
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A DCT-Based Local Dominant Feature Extraction Algorithm for Palm-Print Recognition Hafiz Imtiaz · Shaikh Anowarul Fattah
Received: 15 November 2011 / Revised: 1 September 2012 / Published online: 16 October 2012 © Springer Science+Business Media, LLC 2012
Abstract 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 twodimensional 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. Keywords Feature extraction · Classification · Discrete cosine transform · Entropy-based information content · Dominant spectral feature · Palm-print recognition · Modularization
H. Imtiaz () · S.A. Fattah Department of Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh e-mail: [email protected] S.A. Fattah e-mail: [email protected]
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Circuits Syst Signal Process (2013) 32:1179–1204
1 Introduction Automatic palm-print recognition has widespread applications in security, authentication, surveillance, and criminal identification. Conventional ID card and password based identification methods, although very popular, are no more reliable as before because of the use of several advanced techniques of forgery and password-hacking. As alternatives, biometrics, such as palm-print, finger-print, face and iris, are being used for identity access management as these are not prone to theft and loss, and do not rely on the memory of their users [7]. Among different biometrics, in security applications, the palm-prints have since recently been getting more attention among researchers [11, 12]. The inner surface of the palm normally contains three flexion creases, known as principal lines, and secondary creases and ridges. These complex line patterns are very useful in personal identification. Nevertheless, palm-print recognition is a complicated visual task even for humans. The p
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