Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

Allagwail, Saad Omran Elhashmi; Gedik, O. Serdar; Rahebi, Javad

doi:10.3390/sym11020157

Cited by 24 publications

(18 citation statements)

References 54 publications

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“…The most general definition of the coefficient of determination is Table 2 shows the specifications of the layer that is used in deep learning. [56] 75.12% CRC [69] 79.4% Gabor wavelet with Euclidian method [57] 83.44% Symmetrical face sample method [49] 81.43% Proposed method 85.25%…”

Section: Biomed Research Internationalmentioning

confidence: 99%

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Ahmed

Frikha

Hussein

et al. 2021

BioMed Research International

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In this study, Gabor wavelet transform on the strength of deep learning which is a new approach for the symmetry face database is presented. A proposed face recognition system was developed to be used for different purposes. We used Gabor wavelet transform for feature extraction of symmetry face training data, and then, we used the deep learning method for recognition. We implemented and evaluated the proposed method on ORL and YALE databases with MATLAB 2020a. Moreover, the same experiments were conducted applying particle swarm optimization (PSO) for the feature selection approach. The implementation of Gabor wavelet feature extraction with a high number of training image samples has proved to be more effective than other methods in our study. The recognition rate when implementing the PSO methods on the ORL database is 85.42% while it is 92% with the three methods on the YALE database. However, the use of the PSO algorithm has increased the accuracy rate to 96.22% for the ORL database and 94.66% for the YALE database.

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Section: Biomed Research Internationalmentioning

confidence: 99%

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Ahmed

Frikha

Hussein

et al. 2021

BioMed Research International

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“…• Wavelet transform and GLCM 142,143 • Binary Gabor filter 144 • SVD and DWT transform 145 • Gabor filter and GLCM 146…”

Section: Related Workmentioning

confidence: 99%

Study of statistical methods for texture analysis and their modern evolutions

Ramola

Shakya

Pham

2020

Engineering Reports

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Texture analysis is widely performed in the current time as it is considered as an intimate property of the surface. It is widely used in the field of image processing, remote sensing applications, biomedical analysis, document processing, and so on. In this investigation, we present a detailed study of four different methodologies that have been developed for texture classification. These methodologies include gray level cooccurrence matrix (GLCM), local binary pattern (LBP), autocorrelation function (ACF), and histogram pattern. The detailed investigation on these methods suggests that GLCM is best for analyzing the surface texture, land-use/landcover classification, and satellite data processing. LBP is widely used to analyze the facial features of an individual. The autocorrelation is used to identify the regularity of the textured surface. Finally, through histograms, one can visually identify the changes developed while analyzing the texture of the image data. Furthermore, we present a brief summary for newly developed texture classification techniques such as binary Gabor pattern, local spiking pattern, SRITCSD method, scale inversion, and deep perception models for texture analysis. Some benchmark texture datasets used in image processing are also discussed in this work. K E Y W O R D Sautocorrelation function, gray level cooccurrence matrix, histogram, local binary pattern, texture 1 This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The experimental results showed that the proposed method yields excellent performance compared with several conventional methods from literature. Allagwail et al [32] presented a two-dimensional discrete wavelet transform based on the local binary pattern for face recognition using a symmetry. The introduced method has three main stages: preprocessing, feature extraction and classification.…”

Section: Related Workmentioning

confidence: 99%

Fuzzified Circular Gabor Filter for Circular and Near-Circular Object Detection

et al. 2020

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This paper presents a new algorithm for filtering and detecting circular and deformed circular objects. The resulting procedure is based on fuzzifying the parameters of a circular Gabor filter to optimize the response of the circular Gabor filter and achieve additional selectivity of the filter. Circular and deformed circular objects of interest were efficiently extracted, and considerable resistance to noise and image degradation was achieved. INDEX TERMS Circular Gabor filters, fuzzification of filter parameters, fuzzy system, image filtering, image texture analysis, object detection.

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Face Recognition with Symmetrical Face Training Samples Based on Local Binary Patterns and the Gabor Filter

Cited by 24 publications

References 54 publications

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

[Retracted] Optimum Feature Selection with Particle Swarm Optimization to Face Recognition System Using Gabor Wavelet Transform and Deep Learning

Study of statistical methods for texture analysis and their modern evolutions

Fuzzified Circular Gabor Filter for Circular and Near-Circular Object Detection

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