Feature extraction of face image based on LBP and 2-D Gabor wavelet transform

Zhang, Qian; Li, Hai Gang; Li, Ming; Ding, Lei

doi:10.3934/mbe.2020082

Cited by 22 publications

(14 citation statements)

References 13 publications

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“…Finally the parameter , which is a phase offset in the argument of the cosine factor in Eq (1), determines the symmetry of the concerned Gabor function: for =0 degrees and =180 degrees the function is symmetric, or even; for =-90 degrees and =90 degrees, the function is ant-symmetric, or odd, and all other cases are asymmetric mixtures. As shown in figure 2 observes that Gabor wavelet [1] with 4 scales and 8 orientations and we got 40 kernels.…”

Section: Feature Extraction Using Gabor Transformmentioning

confidence: 84%

“…Before the widespread utilization of deep learning for face recognition, many FR systems relied on feature extraction using the Gabor transform [1]. The wavelet transform was employed to perform multi-resolution time-frequency analysis.…”

Section: Feature Extraction Using Gabor Transformmentioning

confidence: 99%

“…If a chromosome is selected to mutate, we choose randomly a number n of bits to be flipped then n bits are chosen randomly and flipped. To create a large diversity, we set around 10% and n ∈ [1,5]. In Selection step, we implement a probabilistic binary tournament selection.…”

Section: Genetic Algorithmmentioning

confidence: 99%

“…In such cases, we need a fast method that can easily be implemented in an embedded system. So, we use Gabor-wavelets [1] and notice that Gabor wavelet transformation leads to several relatively similar features which may not be optimal for the classifier in terms of both speed and accuracy. The main contributions of this paper are: Firstly, we evaluate six different feature selection methods in terms of the balance between accuracy and the number of features required.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Face Recognition from Small Datasets using Kernel Selection of Gabor Features

Gadelrab¹,

Mohamed²,

El-Melegy³

2020

JES. Journal of Engineering Sciences

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Recent advances in face recognition are mostly based on deep learning methods that require large datasets for training. For smaller datasets, we propose a method that combines Gabor feature extraction and aggressive kernel selection to achieve low error rates while keeping computational cost at a minimum. The paper compares the proposed method against traditional feature selection approaches in terms of the recognition accuracy and model compression and show that the proposed method can achieve the same or higher accuracy with significantly lower computational cost. Moreover, we evaluated combining multiple feature selection algorithms to derive our proposed kernel selection method achieving an error rate of 0.025 on the Yala face dataset.

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Section: Feature Extraction Using Gabor Transformmentioning

confidence: 84%

Section: Feature Extraction Using Gabor Transformmentioning

confidence: 99%

Section: Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Face Recognition from Small Datasets using Kernel Selection of Gabor Features

Gadelrab¹,

Mohamed²,

El-Melegy³

2020

JES. Journal of Engineering Sciences

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show abstract

“…Selection of parameters based on Gabor. The extraction of texture feature in frequency domain is based on Gabor wavelet transform, [12][13][14] which is making the convolution operation of Gabor filter and original image. Then the converted fiber image is extracted texture feature values by adopting the Gray-Scale difference statistics method.…”

Section: Improved Algorithmmentioning

confidence: 99%

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Zhu

Huang

et al. 2021

Journal of Engineered Fibers and Fabrics

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The common texture feature extraction method is only in spatial or frequency domain, leading to insufficient texture information and low accuracy. The main aim of this paper is to present a novel texture feature analysis method based on gray level co-occurrence matrix and Gabor wavelet transform to sufficiently extract texture feature of cashmere and wool fibers. Firstly, the gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method. Then, because the contrast and angle second moment are used as descriptors of fiber image in both spatial and frequency domain, they are fused respectively by introducing a weight to make linear addition, making eight feature values compose a 6-dimensional feature vector. Finally, these feature vectors are fed into the Fisher classifier. The experimental results show that the identification accuracy of the proposed algorithm is improved by 0.682% compared to use 8-dimensional feature vectors describing the sample image. It verifies that the fused method based on texture feature in spatial and frequency domain is an effective approach to identify fibers of cashmere and wool.

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Robust Zero Watermarking Algorithm for Encrypted Medical Images Based on DWT-Gabor

Xiao

Dan

et al. 2021

Innovation in Medicine and Healthcare

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Feature extraction of face image based on LBP and 2-D Gabor wavelet transform

Cited by 22 publications

References 13 publications

Face Recognition from Small Datasets using Kernel Selection of Gabor Features

Face Recognition from Small Datasets using Kernel Selection of Gabor Features

Identification method of cashmere and wool based on texture features of GLCM and Gabor

Robust Zero Watermarking Algorithm for Encrypted Medical Images Based on DWT-Gabor

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