Wavelet based Multimodal Biometrics with Score Level Fusion Using Mathematical Normalization

Sanjekar, Priti S.; Patil, J. B.

doi:10.5815/ijigsp.2019.04.06

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…5. The results of the experiments confirmed the data [21][22][23][24][25][26][27][28] on the significant dependence of the quality of filtering of biometric parameters on the type of basic wavelet. Thus, in Fig.…”

Section: Development Of the Mathematical Apparatussupporting

confidence: 72%

“…Correction of this shortcoming is associated with the use of filtering methods based on wavelet transforms [19,20]. For example, in [21] a mathematical model of parameterization of the structure of the iris of the eye using wavelet transform based on derivatives of the Gaussian function is considered. The results of experimental researches proving efficiency of the offered model are shown.…”

Section: Related Workmentioning

confidence: 99%

“…6. Using expressions (14)(15)(16)(20)(21)(22) to calculate the values of wavelet coefficients of each of the controlled images with minimal detail of wavelet transforms.…”

Section: Criterion Type Of Basic Wavelet Haar Mhat Fhat Wavementioning

confidence: 99%

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Procedure for Processing Biometric Parameters Based on Wavelet Transformations

Hu¹,

Tereikovskyi²,

Chernyshеv³

et al. 2021

IJMECS

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The problem of the article is related to the improvement of means of covert monitoring of the face and emotions of operators of information and control systems on the basis of biometric parameters that correlate with twodimensional monochrome and color images. The difficulty in developing such tools has been shown to be largely due to the cleaning of images associated with biometric parameters from typical non-stationary interference caused by uneven lighting and foreign objects that interfere with video recording. The possibility of overcoming these difficulties by using wavelet transform technology, which is used to filter images by combining several identical, but differently noisy monochrome and color images, is substantiated. It is determined that the development of technology for the use of wavelet transforms is primarily associated with the choice of the type of basic wavelet, the parameters of which must be adapted to the conditions of use in a particular system of covert monitoring of personality and emotions. An approach to choosing the type of basic wavelet that is most effective in filtering images from non-stationary interference is proposed. The approach is based on a number of the proposed provisions and efficiency criteria that allow to ensure when choosing the type of basic wavelet taking into account the significant requirements of the task. A filtering procedure has been developed, which, due to the application of the specified video image filtering technology and the proposed approach to the choice of the basic wavelet type, allows to effectively clean the images associated with biometric parameters from typical non-stationary interference. The conducted experimental studies have shown the feasibility of using the developed procedure for filtering images of the face and iris of operators of information and control systems.

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Section: Development Of the Mathematical Apparatussupporting

confidence: 72%

Section: Related Workmentioning

confidence: 99%

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Procedure for Processing Biometric Parameters Based on Wavelet Transformations

Hu¹,

Tereikovskyi²,

Chernyshеv³

et al. 2021

IJMECS

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“…Sanjekar and Patil [25] presented a method based on a palmprint and fingerprint combination, as well as the fusion of both features at the feature and score levels. Both properties were determined using directional…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion

AlMahafzah

Abukhalil

Alksasbeh

et al. 2023

IJECE

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When it is important to authenticate a person based on his or her biometric qualities, most systems use a single modality (e.g. fingerprint or palm print) for further analysis at higher levels. Rather than using higher levels, this research recommends using two biometric features at the sensor level. The Log-Gabor filter is used to extract features and, as a result, recognize the pattern, because the data acquired from images is sampled at various spacing. Using the two fused modalities, the suggested system attained greater accuracy. Principal component analysis was performed to reduce the dimensionality of the data. To get the optimum performance between the two classifiers, fusion was performed at the sensor level utilizing different classifiers, including <em>k</em>-nearest neighbors and support vector machines. The technology collects palm prints and veins from sensors and combines them into consolidated images that take up less disk space. The amount of memory needed to store such photos has been lowered. The amount of memory is determined by the number of modalities fused.

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“…Total400featuresareextractedfrompalmprintimageandmatchingisperformedusingEuclidean distance.Thefeatureextractionofthelastmodalityi.e.irisisalsobasedonwavelettransformation. ThewavelettransformationusingHaarwaveletiscarriedoutonnormalizedirisimages.Total324 features are extracted from the wavelet transformed iris image and finally hamming distance is usedformatching.Thedetailprocedureoffeatureextractionofallthreemodalitiesisdescribed in previous work (Sanjekar, 2019). Databases used for unimodal biometric verification systems andtheperformanceofeachunimodalverificationsystemintermsofEqualErrorRateisgivenin experimentalresultssection.…”

Section: Unimodal Biometric Verification Systemsmentioning

confidence: 99%

Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

Sanjekar

Patil

2019

International Journal of Computer Vision and Image Processing

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Multimodal biometrics is the frontier to unimodal biometrics as it integrates the information obtained from multiple biometric sources at various fusion levels i.e. sensor level, feature extraction level, match score level, or decision level. In this article, fingerprint, palmprint, and iris are used for verification of an individual. The wavelet transformation is used to extract features from fingerprint, palmprint, and iris. Further the PCA is used for dimensionality reduction. The fusion of traits is employed at three levels: feature level; feature level combined with match score level; and feature level combined with decision level. The main objective of this research is to observe effect of combined fusion levels on verification of an individual. The performance of three cases of fusion is measured in terms of EER and represented with ROC. The experiments performed on 100 different subjects from publicly available databases demonstrate that combining feature level with match score level and feature level with decision level fusion both outperforms fusion at only a feature level.

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Wavelet based Multimodal Biometrics with Score Level Fusion Using Mathematical Normalization

Cited by 6 publications

References 22 publications

Procedure for Processing Biometric Parameters Based on Wavelet Transformations

Procedure for Processing Biometric Parameters Based on Wavelet Transformations

Multi-modal palm-print and hand-vein biometric recognition at sensor level fusion

Multimodal Biometrics Using Fingerprint, Palmprint, and Iris With a Combined Fusion Approach

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