Information Measures for Biometric Identification via 2D Discrete Wavelet Transform

Ye, Zhengmao; Mohamadian, Habib; Ye, Yongmao

doi:10.1109/coase.2007.4341670

Cited by 34 publications

(15 citation statements)

References 11 publications

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“…Ā T Ā = I (5) To reconstruct independent patterns after dimension reduction, optimization can be conducted as (6).…”

Section: Biometric Independent Component Analysismentioning

confidence: 99%

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Quantitative comparison of two nonlinear approaches for synthetic biometric verification

Ye¹,

Mohamadian²,

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

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Biometric verification is one of the leading security issues. Being the unique signature (e.g., fingerprint) of human beings, biometric pattern recognition serves as an interesting branch of study in disciplines of digital image processing. Due to the fact that the accuracy of pattern recognition determines the correctness of decision making, two nonlinear approaches have been introduced, where independent component analysis (ICA) and discrete wavelet transform (DWT) are among the most effective methods. After sensing information is digitized into image matrix files, data matrices could be analyzed using ICA algorithms. 2D DWT is capable of compressing fingerprints and then reconstructing patterns by reorganizing approximation components and revised components of horizontal detail, vertical detail and diagonal detail via thresholding. Satisfactory results can be obtained from both approaches, however, it is hard to show which approach could be better performed from visual appealing. In this case, one set of quantitative measures is presented to evaluate outcomes of biometric verification on a basis of ICA and 2D DWT, where the objective measures of the discrete entropy, discrete energy, relative entropy, contrast, homogeneity, dissimilarity and mutual information are used to illustrate drawbacks and merits of these two dominating biometric recognition approaches.

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“…Ā T Ā = I (5) To reconstruct independent patterns after dimension reduction, optimization can be conducted as (6).…”

Section: Biometric Independent Component Analysismentioning

confidence: 99%

“…The non-orthogonal wavelet transform is better than orthogonal transform in that it avoids the artifacts in reconstruction due to downsampling. 2D discrete wavelet transform (DWT) is appropriate for biometric fingerprint verification problems [6][7].…”

Section: Introductionmentioning

confidence: 99%

Quantitative comparison of two nonlinear approaches for synthetic biometric verification

Ye¹,

Mohamadian²,

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

Self Cite

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“…There appear many algorithms and techniques proposed and applied to fingerprint image enhancement: using Fourier transform [4,5], Gabor filters [2,6], Wavelet transform [7,8,9,10,11], and minutiae filtering, applied to binary [12] or gray-scale images [13]. The main of an enhancement algorithm is to improve the clarity of ridge structures of fingerprint images in recoverable regions and to remove the unrecoverable regions.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Image Enhancement with Second Derivative Gaussian Filter and Directional Wavelet Transform

Sihalath

Choomchuay

Wada

et al. 2010

2010 Second International Conference on Computer Engineering and Applications

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In this paper, we propose a technique for enhancing the quality of fingerprint images. Directional wavelet transform and second derivative of a Gaussian filter are applied. The original fingerprint image is decomposed into approximation and detail sub-images. To each sub-dimension a directional filter: second derivative of Gaussian filter is applied for tuning up the image features. The enhanced image is measured for its improvement by testing the success of core point identification where Poincare technique is used. The commonly-well-known database FVC-2004 is used in this study. The obtained results offer clean visualization as well as the increase the success of true core point detection.

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“…The actual output of the layer is given by a i . Thus the error or cost function is given by [21] 2 2 ) ( 2…”

Section: 1mentioning

confidence: 99%

Performance Analysis of Neural Network Architecture Combined with DWT for Image Compression

S¹,

Satyanarayana²

2012

IJCA

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Neural networks are significantly used in signal and image processing techniques for pattern recognition and template matching. In this work neural networks are used for image compression. In order to improve the performances image compression algorithm, DWT is combined with NN for achieving better MSE and increase in compression ration greater than 100%. NN architecture achieves maximum of 98% with use of four neurons in the hidden layer, with selection of LL sub band only the compression is improved by another 75%. The proposed architecture is analyzed for 20 images and MSE is found to be improved by a factor of 20%. Daubechies wavelet filter and Haar wavelet filters are used for DWT, input layer with one hidden layer and output layer consisting of tansig and purelin function us used for compression. The design proposed is suitable for high resolution image compression.

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Information Measures for Biometric Identification via 2D Discrete Wavelet Transform

Cited by 34 publications

References 11 publications

Quantitative comparison of two nonlinear approaches for synthetic biometric verification

Quantitative comparison of two nonlinear approaches for synthetic biometric verification

Fingerprint Image Enhancement with Second Derivative Gaussian Filter and Directional Wavelet Transform

Performance Analysis of Neural Network Architecture Combined with DWT for Image Compression

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