Complexity Evaluation of the Ateb-Gabor Filtration Algorithm in Biometric Security Systems

Nazarkevych, Mariya; Lotoshynska, Nataliia; Klyujnyk, Ivanna; Voznyi, Yaroslav; Forostyna, Sofiya; Maslanych, Iryna

doi:10.1109/ukrcon.2019.8879945

Cited by 11 publications

(3 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, in the daytime, we do not see stars in the sky, although the absolute contrast of the sky -stars are not more than ten thousand. The fact that the contrast sensitivity of the human eye is only 2% [19], so the noticeable absolute contrast does not exceed 50. Individual parts of the billion-per-person perception range can only be considered in turn, adapting to each section of illumination.…”

Section: Perception Of the Spectrum Problemmentioning

confidence: 99%

Real-Time Sensing, Reasoning and Adaptation for Computer Vision Systems

Hrytsyk

Nazarkevych

2021

Lecture Notes in Computational Intelligence and Decision Making

View full text Add to dashboard Cite

Automatically recognition and classification of biological objects under microscope methods are shown in paper. Problem of separated of white-black and color images is studied. Method of separation of different type of objects (visual diapason of specter) with compare results is shown in the paper. Quality of segmentation methods analyses is presented in the paper. Schemes and table results of segmentation are exist. Methods of pattern recognition applicability for Computer Vision Systems of analysis and pattern recognition scenes in the visual spectrum are studied in the paper. The methods and algorithms can be used in Realtime Sensing, white-black and color patterns, reasoning and adaptation for Computer Vision Systems too. Example of such systems is the glasses for people with visual impairments; when the camera mounted in glasses receives and transmits environment data, and the contact plate with electrical leads via e-pulse transmits data to the eye retina. Author analyzed several pattern recognition methods that will allow to process data of the environment for the brain. This will make the visually impaired persons with sub reality vision better orientation in environment. Theoretical basic, algorithms and their compared for apply is presented in paper.

show abstract

Section: Perception Of the Spectrum Problemmentioning

confidence: 99%

Real-Time Sensing, Reasoning and Adaptation for Computer Vision Systems

Hrytsyk

Nazarkevych

2021

Lecture Notes in Computational Intelligence and Decision Making

View full text Add to dashboard Cite

show abstract

“…У цьому рівнянні λдовжина хвилі множника косинуса, θв градусах, ψзсув фаз у градусах, та φкоефіцієнт стиснення [7]. Формула представляє собою добуток функції Гауса та періодичної функції, що є покращенням монотонних областей періодичних зображень [11]. Для застосування фільтрації необхідно знати перераховані вище параметри фільтра Габора.…”

Section: ʟˈˊ˖ˎ˟˕˃˕ˋ ˇˑ˔ˎ˪ˇˉˈːːˢ ˕˃ ˫˘ ˑ˄ˆˑ˅ˑ˓ˈːːˢunclassified

Development of Biometric Identification Methods Based on New Filtration Methods

Nazarkevych¹

2021

UJIT

View full text Add to dashboard Cite

The article is devoted to the development of biometric identification methods based on new filtration methods. Biometric identification systems need constant improvement, because they often work slowly and give the wrong result. To increase the reliability of biometric image recognition, the method is formed, which is formed from the stages: segmentation, normalization, local orientation estimation, local estimation, spine frequency estimation, Gabor filter implementation, binarization, thinning. A new filtering method is proposed, which is based on a new type of function – Ateb-functions, which are used next to the Gabor filter. The local orientation can be calculated from local gradients using the arctangent function. The normalization process is performed to evenly redistribute the values of image intensity. When segmenting, the foreground areas in the image are separated from the background areas. A new method of wavelet conversion of biometric image filtering based on Ateb-Gabor has been developed. The Gabor filter is used for linear filtering and improves the quality of the converted image. Symmetry and wavelet transform operations are also used to reduce the number of required multiplication and addition operations. The method is based on the well-known Gabor filter and allows you to rearrange the image with clearer contours. Therefore, this method is applicable to biometric images, where the creation of clear contours is particularly relevant. When Gabor filtering, the image is reconstructed by multiplying the harmonic function by the Gaussian function. Ateb functions are a generalization of elementary trigonometry, and, accordingly, have greater functionality. Ateb-Gabor filtering allows you to change the intensity of the whole image, as well as the intensity in certain ranges, and thus make certain areas of the image more contrasting. Filtering with Ateb functions allows you to change the image from two rational parameters. This allows you to more flexibly manage filtering and choose the best options. When you perform a thinning, the foreground pixels are erased until there is one pixel wide. A standard thinning algorithm is used, or the thinning developed by the authors in other studies. This filtering will provide more accurate characteristics, as it allows you to get more sloping shapes and allows you to organize a wider range of curves. Numerous experimental studies indicate the effectiveness of the proposed method.

show abstract

“…The known component of a method error, the difference between the exact value and the ones found by Formula (4), can also be calculated by Equation ( 8), but only for each of the N support vectors. However, this formula shows that the response surface of an error is sufficiently smooth [45,46] and, therefore, can be simulated somehow in the local region of the space of input variables. As experiments confirmed, the use of another GRNN network with a reduced value of a smooth factor σ provides a satisfactory approximation of the method error.…”

Section: Components Of Grnn Output Generation Errormentioning

confidence: 99%

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

Tkachenko

Izonin

Kryvinska

et al. 2020

Sensors

View full text Add to dashboard Cite

The purpose of this paper is to improve the accuracy of solving prediction tasks of the missing IoT data recovery. To achieve this, the authors have developed a new ensemble of neural network tools. It consists of two successive General Regression Neural Network (GRNN) networks and one neural-like structure of the Successive Geometric Transformation Model (SGTM). The principle of ensemble topology construction on two successively connected general regression neural networks, supplemented with an SGTM neural-like structure, is mathematically substantiated, which improves the accuracy of prediction results. The effectiveness of the method is based on the replacement of the summation of the results of the two GRNNs with a weighted summation, which improves the accuracy of the ensemble operation in general. A detailed algorithmic implementation of the ensemble method as well as a flowchart of its operation is presented. The parameters of the ensemble operation are determined by optimization using the brute-force method. Based on the developed ensemble method, the solution of the task of completing the partially missing values in the real monitoring dataset of the air environment collected by the IoT device is presented. By comparing the performance of the developed ensemble with the existing methods, the highest accuracy of its performance (by the parameters of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) accuracy) among the most similar in this class has been proved.

show abstract

Complexity Evaluation of the Ateb-Gabor Filtration Algorithm in Biometric Security Systems

Cited by 11 publications

References 20 publications

Real-Time Sensing, Reasoning and Adaptation for Computer Vision Systems

Real-Time Sensing, Reasoning and Adaptation for Computer Vision Systems

Development of Biometric Identification Methods Based on New Filtration Methods

An Approach towards Increasing Prediction Accuracy for the Recovery of Missing IoT Data based on the GRNN-SGTM Ensemble

Contact Info

Product

Resources

About