WAVELET TRANSFORMATION ATEB-GABOR FILTERS TO BIOMETRIC IMAGES

Biometric images were pre-processed and filtered in two ways, by wavelet- Gabor and wavelet Ateb-gabor filtration. Ateb-based Gabor filter is effective for filtration because it contains generalizations of trigonometric functions. The wavelet transform of Ateb-Gabor function was developed. The function dependence on seven parameters was shown, each of them significantly changes the filtering results of biometric images. The Ateb-Gabor wavelet research was performed. Graphic dependencies of the wavelet Gabor filter and the wavelet Ateb-Gabor filter were constructed. The appliance of wavelet transform makes it possible to reduce the complexity of calculating an Ateb-Gabor filter by simplifying function calculations and reducing filtering time. The complexities of algorithms for calculating the wavelet Gabor filter and the wavelet Ateb-Gabor filter have been evaluated. Ateb-Gabor filtration allows you to adjust the intensity of the entire image, and to change certain ranges, thereby changing certain areas of the image. Biometric images should have this property, on which the minucius should be contrasting and clear. Ateb functions have the property of changing two rational parameters, which will allow to make more flexible control of filtration. The properties of the Ateb function, as well as the possibility of changing the amplitude of the function, the oscillation frequency by the numerical values of the Ateb-Gabor filter, were investigated. By using the parameters of the Ateb function, you can get a much larger range of shapes and sizes, which expands the number of possible filtration options. You can also perform filtration once, taking into account the direction of the minucius and reliably determine the sharpness of the edges, rather than perform filtration many times. The reliability of results were tested using NIST Special Database 302 and good filtration results were shown. This is confirmed by the comparison experiment between the wavelet-Gabor filter and the wavelet Ateb-Gabor function based on the PSNR signal-to-noise ratio measurement.

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DEVELOPMENT OF MACHINE LEARNING METHOD WITH BIOMETRIC PROTECTION WITH NEW FILTRATION METHODS

Cybersecurity Education Science Technique ◽

10.28925/2663-4023.2021.11.1630 ◽

2021 ◽

Vol 3 (11) ◽

pp. 16-30

Author(s):

Mariya Nazarkevych ◽

Yaroslav Voznyi ◽

Hanna Nazarkevych

Keyword(s):

Machine Learning ◽

Wavelet Transforms ◽

Gabor Filter ◽

Signal To Noise Ratio ◽

Gabor Wavelet ◽

Gabor Filtering ◽

Flexible Control ◽

Gabor Function ◽

Machine Learning Methods ◽

Good Filtration

Biometric images were processed and filtered by a newly developed Ateb-Gabor wavelet filter. Identification of biometric images was performed by machine learning methods. The Gabor filter based on Ateb functions is effective for filtering because it contains generalizations of trigonometric functions. Developed wavelet transform of Ateb-Gabor function. It is shown that the function depends on seven parameters, each of which makes significant changes in the results of filtering biometric images. A study of the wavelet Ateb-Gabor function was performed. The graphical dependences of the Gabor filter wavelet and the Ateb-Gabor filter wavelet are constructed. The introduction of wavelet transforms reduces the complexity of Ateb-Gabor filter calculations by simplifying function calculations and reducing filtering time. The complexity of the algorithms for calculating the Gabor filter wavelet and the Ateb-Gabor filter wavelet is evaluated. Ateb-Gabor filtering allows you to change the intensity of the entire image, and to change certain ranges, and thus change certain areas of the image. It is this property that biometric images should have, in which the minions should be contrasting and clear. Ateb functions have the ability to change two rational parameters, which, in turn, will allow more flexible control of filtering. The properties of the Ateb function are investigated, as well as the possibility of changing the amplitude of the function, the oscillation frequency to the numerical values of the Ateb-Gabor filter. By using the parameters of the Ateb function, you can get a much wider range of shapes and sizes, which expands the number of possible filtering options. You can also implement once filtering, taking into account the direction of the minutes and reliably determine the sharpness of the edges, rather than filtering batocrates. The reliability results were tested on the basis of NIST Special Database 302, and good filtration results were shown. This was confirmed by a comparison experiment between the Wavelet-Gabor filtering and the Ateb-Gabor wavelet function based on the measurement of the PSNR signal-to-noise ratio.

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Applying Ateb–Gabor Filters to Biometric Imaging Problems

Symmetry ◽

10.3390/sym13040717 ◽

2021 ◽

Vol 13 (4) ◽

pp. 717

Author(s):

Mariia Nazarkevych ◽

Natalia Kryvinska ◽

Yaroslav Voznyi

Keyword(s):

Wavelet Transformation ◽

Symmetric Functions ◽

Gabor Filter ◽

Signal To Noise Ratio ◽

The Other ◽

Mean Square ◽

Image Transformation ◽

Gabor Filtering ◽

Signal To Noise ◽

Filtration Method

This article presents a new method of image filtering based on a new kind of image processing transformation, particularly the wavelet-Ateb–Gabor transformation, that is a wider basis for Gabor functions. Ateb functions are symmetric functions. The developed type of filtering makes it possible to perform image transformation and to obtain better biometric image recognition results than traditional filters allow. These results are possible due to the construction of various forms and sizes of the curves of the developed functions. Further, the wavelet transformation of Gabor filtering is investigated, and the time spent by the system on the operation is substantiated. The filtration is based on the images taken from NIST Special Database 302, that is publicly available. The reliability of the proposed method of wavelet-Ateb–Gabor filtering is proved by calculating and comparing the values of peak signal-to-noise ratio (PSNR) and mean square error (MSE) between two biometric images, one of which is filtered by the developed filtration method, and the other by the Gabor filter. The time characteristics of this filtering process are studied as well.

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Image Intelligent Detection Based on the Gabor Wavelet and the Neural Network Combined Neural Network

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2016.5673 ◽

2016 ◽

Vol 13 (10) ◽

pp. 7074-7079

Author(s):

Yajun Xu ◽

Fengmei Liang ◽

Gang Zhang ◽

Huifang Xu

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Network Model ◽

Neural Network Model ◽

Gabor Wavelet ◽

Two Dimensional ◽

Human Face ◽

Gabor Wavelet Transform ◽

Gabor Function ◽

The Neural Network

This paper first analyzes the one-dimensional Gabor function and expands it to a two-dimensional one. The two-dimensional Gabor function generates the two-dimensional Gabor wavelet through measure stretching and rotation. At last, the two-dimensional Gabor wavelet transform is employed to extract the image feature information. Based on the BP neural network model, the image intelligent test model based on the Gabor wavelet and the neural network model is built. The human face image detection is adopted as an example. Results suggest that, when the method combining Gabor wavelet transform and the neural network is used to test the human face, it will not influence the detection results despite of complex textures and illumination variations on face images. Besides, when ORL human face database is used to test the model, the human face detection accuracy can reach above 0.93.

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A Gabor Wavelet Transformation-Based Texture Images Classification Algorithm

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.811.430 ◽

2013 ◽

Vol 811 ◽

pp. 430-434

Author(s):

Hai Feng Wang ◽

Kun Zhang ◽

Hong E Ren

Keyword(s):

Wavelet Transform ◽

Wavelet Transformation ◽

Texture Feature ◽

Near Neighbor ◽

Classification Algorithm ◽

Gabor Wavelet ◽

Accuracy Rate ◽

Gabor Wavelet Transform ◽

Texture Image Classification ◽

Gabor Wavelet Transformation

In this paper, we introduce a texture image classification algorithm based on Gabor wavelet transform. Using Gabor wavelet transform, image is decomposed into sub-bands images in multiresolution and multi-direction, and we extract texture feature from all sub-bands images. Then the algorithm groups feature image into clusters by the k near neighbor algorithm. The experimental results on dataset Brodatz showed that the proposed algorithm can achieve an ideal accuracy rate and excellent classification effect.

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MEAN AND STANDARD DEVIATION AS FEATURES FOR PALMPRINT RECOGNITION BASED ON GABOR FILTERS

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001411008750 ◽

2011 ◽

Vol 25 (04) ◽

pp. 491-512 ◽

Cited By ~ 9

Author(s):

MEIRU MU ◽

QIUQI RUAN

Keyword(s):

Feature Extraction ◽

Wavelet Transform ◽

Standard Deviation ◽

Gabor Filter ◽

Recognition Performance ◽

Gabor Filters ◽

Gabor Wavelet ◽

Palmprint Recognition ◽

Linear Discriminant ◽

Gabor Wavelet Transform

The two-dimensional (2D) Gabor function has been recognized as a very useful tool in feature extraction of image, due to its optimal localization properties in both spatial and frequency domain. This paper presents a novel palmprint feature extraction method based on the statistics of decomposition coefficients of the Gabor wavelet transform. It is experimentally found that the magnitude coefficients of the Gabor wavelet transform within each subband uniformly to approximate the Lognormal distribution. Based on this fact, we create the palmprint representation using two simple statistics (mean and standard deviation) as feature components after applying the logarithmic transformation of Gabor filtered magnitude coefficients for each subband with different orientations and scales. The optimum setting of the number of Gabor filters and orientation of each Gabor filter is experimentally determined. For palmprint recognition, the popularly used Fisher Linear Discriminant (FLD) analysis is further applied on the constructed feature vectors to extract discriminative features and reduce dimensionality. All experiments are both executed over the CCD-based HongKong PolyU Palmprint Database of 7752 images and the scanner-based BJTU_PalmprintDB (V1.0) of 3460 images. The results demonstrate the effectiveness of the proposed palmprint representation in achieving the improved recognition performance.

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Adaptive identification methods of nonlinear random electrical signal

International Journal of Applied Electromagnetics and Mechanics ◽

10.3233/jae-209349 ◽

2020 ◽

Vol 64 (1-4) ◽

pp. 431-438

Author(s):

Jian Liu ◽

Lihui Wang ◽

Zhengqi Tian

Keyword(s):

Wavelet Transform ◽

Electric Vehicle ◽

Signal To Noise Ratio ◽

Sudden Change ◽

Electrical Signal ◽

Signal Recognition ◽

State Variable ◽

Signal Characteristics ◽

The Matrix ◽

Kalman Filter Algorithm

The nonlinearity of the electric vehicle DC charging equipment and the complexity of the charging environment lead to the complex and changeable DC charging signal of the electric vehicle. It is urgent to study the distortion signal recognition method suitable for the electric vehicle DC charging. Focusing on the characteristics of fundamental and ripple in DC charging signal, the Kalman filter algorithm is used to establish the matrix model, and the state variable method is introduced into the filter algorithm to track the parameter state, and the amplitude and phase of the fundamental waves and each secondary ripple are identified; In view of the time-varying characteristics of the unsteady and abrupt signal in the DC charging signal, the stratification and threshold parameters of the wavelet transform are corrected, and a multi-resolution method is established to identify and separate the unsteady and abrupt signals. Identification method of DC charging distortion signal of electric vehicle based on Kalman/modified wavelet transform is used to decompose and identify the signal characteristics of the whole charging process. Experiment results demonstrate that the algorithm can accurately identify ripple, sudden change and unsteady wave during charging. It has higher signal to noise ratio and lower mean root mean square error.

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Evaluvation of Multiresolution Watermarking Algorithm

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v7i8.29 ◽

2017 ◽

Vol 7 (8) ◽

pp. 85

Author(s):

S. Thabasu Kannan ◽

S. Azhagu Senthil

Keyword(s):

Digital Watermarking ◽

Wavelet Transformation ◽

Signal To Noise Ratio ◽

Rapid Development ◽

Threshold Value ◽

Discrete Wavelet ◽

Digital Production ◽

Common Framework ◽

Sampling Operation ◽

Watermarking Scheme

Now-a-days watermarking plays a pivotal role in most of the industries for providing security to their own as well as hired or leased data. This paper its main aim is to study the multiresolution watermarking algorithms and also choosing the effective and efficient one for improving the resistance in data compression. Computational savings from such a multiresolution watermarking framework is obvious. The multiresolutional property makes our watermarking scheme robust to image/video down sampling operation by a power of two in either space or time. There is no common framework for multiresolutional digital watermarking of both images and video. A multiresolution watermarking based on the wavelet transformation is selected in each frequency band of the Discrete Wavelet Transform (DWT) domain and therefore it can resist the destruction of image processing. The rapid development of Internet introduces a new set of challenging problems regarding security. One of the most significant problems is to prevent unauthorized copying of digital production from distribution. Digital watermarking has provided a powerful way to claim intellectual protection. We proposed an idea for enhancing the robustness of extracted watermarks. Watermark can be treated as a transmitted signal, while the destruction from attackers is regarded as a noisy distortion in channel. For the implementation, we have used minimum nine coordinate positions. The watermarking algorithms to be taken for this study are Corvi algorithm and Wang algorithm. In all graph, we have plotted X axis as peak signal to noise ratio (PSNR) and y axis as Correlation with original watermark. The threshold value ά is set to 5. The result is smaller than the threshold value then it is feasible, otherwise it is not.

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Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) ◽

10.2174/2352096511666180511151646 ◽

2019 ◽

Vol 12 (2) ◽

pp. 138-151

Author(s):

Mourad Talbi ◽

Med Salim Bouhlel

Keyword(s):

Wavelet Transform ◽

Speech Signal ◽

Signal To Noise Ratio ◽

Perceptual Quality ◽

Lifting Wavelet Transform ◽

Signal To Noise ◽

Perceptual Evaluation ◽

Lifting Wavelet ◽

Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

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