Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems

Nair, Sanjay; Aruna, P.

doi:10.1016/j.aej.2015.07.002

Cited by 17 publications

(7 citation statements)

References 18 publications

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“…After a brief discussion on computational complexities, the similarity of original host images and watermarked images is measured by the standard correlation coefficient (Corr) as [26]: Moreover, the peak signal-to-noise ratio (PSNR) is used to evaluate the quality of the watermarked images as [27]: …”

Section: Analysis Of Proposed Algorithm and Experimental Resultsmentioning

confidence: 99%

Non Correlation DWT Based Watermarking Behavior in Different Color Spaces

Khalili¹,

Nazari²

2016

ijacsa

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Abstract-There are two digital watermarking techniques. Digital watermarking techniques based on correlation and digital watermarking techniques that are not based on correlation. In previous work, we proposed a DWT2 based CDMA image watermarking scheme to study the effects of using eight color spaces RGB, YCbCr, JPEG-YCbCr, YIQ, YUV, HSI, HSV and CIELab, on watermarking algorithms based on correlation techniques. This paper proposes a non correlation based image watermarking scheme in wavelet transform domain and tests it in the same color spaces, to develop studying, reach a comprehensive analysis and focus on satisfying the requirements of based non coloration watermarking algorithms. To achieve more security, imperceptibility and robustness of the proposed scheme, first, the binary watermark image encodes by applying ATM, CCM and exclusive OR. Then, the scrambled watermark embeds into intended quantized approximation coefficients of wavelet transform by LSB insertion technique.

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Section: Analysis Of Proposed Algorithm and Experimental Resultsmentioning

confidence: 99%

Non Correlation DWT Based Watermarking Behavior in Different Color Spaces

Khalili¹,

Nazari²

2016

ijacsa

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“…For uniform saving format, the local features of each sub-band of image can be expressed by applying the first two singular values. A is the matrix, rows is m, columns is n, and r is the rank (r ≤ n ≤ m) three matrices can be factored [29]:…”

Section: Analysing Image Using Three Transformation Methods (Dct Dwtmentioning

confidence: 99%

Efficient Classifying and Indexing for Large Iris Database Based on Enhanced Clustering Method

Khalaf¹,

Mohammed²,

Moorthy³

et al. 2019

STUD INFORM CONTROL

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Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. A new method is presented in this paper to extract the most relevant features of iris biometric images for indexing the iris database. Three transformation methods DCT, DWT and SVD were used to analyse the iris image and to extract its local features. The clustering method shouldering on the responsibility of determining the partitioning and classification efficiencies of the system has been improved. In the current work, the new Weighted K-means algorithm based on the Improved Firefly Algorithm (WKIFA) has been used to overcome the shortcomings in using the Fireflies Algorithm (FA). The proposed method can be used to perform global search and exhibits quick convergence rate while optimizing the initial clustering centers of the K-means algorithm. From the experimental results, the proposed method was indeed more effective for clustering and classification and outperformed the traditional k-mean algorithm. The Penetration Rates underwent reductions and reached the levels of 0.98, 0.13 and 0.12 for three different databases. Also, the Bin Miss Rates decreased to 0.3037, 0.4226 and 0.2019 for the investigated databases.

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“…In the proposed indexing model, the 8 × 8 block is decomposed into seven subbands by the two‐level DWT. For uniform saving format, the first two SVs can be applied to the expression features of each subband image, for a matrix A with m rows, n columns, and rank r ( r ≤ n ≤ m ), A can be factorised into three matrices [25], (4) and (5):

A = U Σ {bold-italicV}^{normalT}

where matrix U is an m × m orthogonal matrix, matrix V an n × n orthogonal matrix, and Σ an m × n diagonal matrix with SVs on the diagonal with non‐negative numbers

A = \begin{matrix} 1em4pt \\ (][, \begin{matrix} 1em4pt \\ | & | \\ u_{1} & \dots & u_{2} \\ | & | \end{matrix}) \\ m \times m \end{matrix} \begin{matrix} 1em4pt \\ (][, \begin{matrix} 1em4pt \\ σ_{1} \\ ⋱ \\ σ_{p} \end{matrix}) \\ m \times n \end{matrix} \begin{matrix} 1em4pt \\ (][, \begin{matrix} 1em4pt \\ - & V_{1}^{normalT} & - \\ ⋮ \\ - & V_{n}^{normalT} & - \end{matrix}) \\ n \times n \end{matrix}

where

σ_{1} \geq σ_{2} \geq \dots \geq σ_{p}

.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…In the proposed indexing model, the 8 × 8 block is decomposed into seven subbands by the twolevel DWT. For uniform saving format, the first two SVs can be applied to the expression features of each subband image, for a matrix A with m rows, n columns, and rank r (r ≤ n ≤ m), A can be factorised into three matrices [25], (4) and 5:…”

Section: Applying Svdmentioning

confidence: 99%

Robust partitioning and indexing for iris biometric database based on local features

2018

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Explosive growth in the volume of stored biometric data has resulted in classification and indexing becoming important operations in image database systems. Consequently, researchers are focused on finding suitable features of images that can be used as indexes. Stored templates have to be classified and indexed based on these extracted features in a manner that enables access to and retrieval of those data by efficient search processes. This paper proposes a method that extracts the most relevant features of iris images to facilitate minimisation of the indexing time and the search area of the biometric database. The proposed method combines three transformation methods DCT, DWT and SVD to analyse iris images and extract their local features. Further, the scalable K-means++ algorithm is used for partitioning and classification processes, and an efficient parallel technique that divides the features groups causing the formation of two b-trees based on index keys is applied for search and retrieval. Moreover, search within a group is achieved using a proposed half search algorithm. Experimental results on three different publicly iris databases indicate that the proposed method results in a significant performance improvement in terms of bin miss rate and penetration rate compared with conventional methods.

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Comparison of DCT, SVD and BFOA based multimodal biometric watermarking systems

Cited by 17 publications

References 18 publications

Non Correlation DWT Based Watermarking Behavior in Different Color Spaces

Non Correlation DWT Based Watermarking Behavior in Different Color Spaces

Efficient Classifying and Indexing for Large Iris Database Based on Enhanced Clustering Method

Robust partitioning and indexing for iris biometric database based on local features

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