Learning features from covariance matrix of gabor wavelet for face recognition under adverse conditions

Li, Chaorong; Huang, Yuanyuan; Huang, Wei; Qin, Fengqing

doi:10.1016/j.patcog.2021.108085

Cited by 42 publications

(24 citation statements)

References 29 publications

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“…This complicates the task of training processing algorithms. We measured the recognition accuracy of the LWKPCA method proposed by this experimental protocol [3].…”

Section: B Experiments On Lfw and Youtube Celebrities Data Setsmentioning

confidence: 99%

“…LGBPHS [72], PCANet [73], VGGFace [74], SENet+LBP [75], Light-CNN [76], LCMoG-LWPZ [3], LCMoG-CNN [3], LCMoG-(LWPZ+CNN) [3]. The recognition performance of this comparison are illustrated in Table 8.…”

Section: B Experiments On Lfw and Youtube Celebrities Data Setsmentioning

confidence: 99%

“…The application of these descriptors still not accurate to produce optimal and discriminating features. For example, in [3] the authors propose an efficient FR method under adverse conditions based on the Gabor wavelet. In another work, Gabor filters, PCA and ICA are used for FR [4].…”

Section: Introductionmentioning

confidence: 99%

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LWKPCA: A New Robust Method for Face Recognition Under Adverse Conditions

et al. 2022

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Over the last two decades, face recognition (FR) has become one of the most prevailing biometric applications for effective people identification as it offers practical advantages over other biometric modalities. However, current state-of-the-art findings suggest that FR under adverse and challenging conditions still needs improvements. This is because face images can contain many variations like face expression, pose, and illumination. To overcome the effect of these challenges, it is necessary to use representative face features using feature extraction methods. In this paper, we present a new feature extraction method for robust FR called Local Binary Pattern and Wavelet Kernel PCA (LWKPCA). The proposed method aims to extract the discriminant and robust information to minimize recognition errors. This is obtained first by the best use of nonlinear projection algorithm called RKPCA. Then, we adapted the algorithm to reduce the dimensionality of features extracted using the proposed Color Local Binary Pattern and Wavelets transformation called Color LBP and Wavelet Descriptor. The general idea of our descriptor is to find the best representation of face image in a discriminant vector structure by a novel feature grouping strategy generated by the Three-Level decomposition of Discrete Wavelet Transform (2D-DWT) and Local Binary Pattern (LBP). Extensive experiments on four well-known face datasets namely ORL, GT, LFW , and YouTube Celebrities show that the proposed method has a recognition accuracy of 100% for ORL, 96.84% for GT, 99.34% for LFW, and 95.63% for YouTube Celebrities.

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“…This complicates the task of training processing algorithms. We measured the recognition accuracy of the LWKPCA method proposed by this experimental protocol [3].…”

Section: B Experiments On Lfw and Youtube Celebrities Data Setsmentioning

confidence: 99%

Section: B Experiments On Lfw and Youtube Celebrities Data Setsmentioning

confidence: 99%

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LWKPCA: A New Robust Method for Face Recognition Under Adverse Conditions

et al. 2022

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“…As technology advances and data volumes increase, artificial intelligence techniques play an increasingly important role in automatic face recognition. Recognition of faces with machine learning techniques is challenging due to the various complex factors such as facial expression, image noise, head poses and illumination conditions, sensitivity to geometrical variations such as scale, rotation, and translation, the large volume of data, and small training sample size [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…The performance of the FR method depends on its robustness against the mentioned adverse conditions. Gabor textural filters inspired by the functioning of the *Corresponding Author: b_asghari@email.kntu.ac.ir (B. Asghari Beirami) mammalian visual cortices are robust under some adverse conditions [2]. Gabor textural features are usually generated by convolving the Gabor filters in the different scales and orientations with the original face image.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Log-Euclidean Kernel SVM based on Covariance Descriptors of Multiscale Gabor Features for Face Recognition

Beirami

Mokhtarzade

2022

IJE

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Face recognition (FR) is a challenging computer vision task due to various adverse conditions. Local features play an important role in increasing the recognition rate of an FR method. In this direction, the covariance descriptors of Gabor wavelet features have been one of the most prominent methods for accurate FR. Most existing methods rely on covariance descriptors of Gabor magnitude features extracted from single-scale face images. This study proposes a new method named multiscale Gabor covariance-based ensemble Log-euclidean SVM (MGcov-ELSVM) for FR that uses the covariance descriptors of Gabor magnitude and phase features derived from multiscale face representations. MGcov-ELSVM begins by producing multiscale face representations. Gabor magnitude and phase features are derived from the multiscale face images in the second stage. After that, the Gabor magnitude and phase features are used to generate covariance descriptors. Finally, Covariance descriptors are classified via a log-Euclidean SVM classifier, and a majority voting technique determines the final recognition results. The experimental results from two face databases, ORL and Yale, indicate that the MGcov-ELSVM outperforms some recent FR methods.

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MR Image Denoising Based on Improved Multipath Matching Pursuit Algorithm

Luo

Yang

et al. 2022

Lecture Notes in Computer Science

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Learning features from covariance matrix of gabor wavelet for face recognition under adverse conditions

Cited by 42 publications

References 29 publications

LWKPCA: A New Robust Method for Face Recognition Under Adverse Conditions

LWKPCA: A New Robust Method for Face Recognition Under Adverse Conditions

Ensemble of Log-Euclidean Kernel SVM based on Covariance Descriptors of Multiscale Gabor Features for Face Recognition

MR Image Denoising Based on Improved Multipath Matching Pursuit Algorithm

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