Two novel color local descriptors for face recognition

Karanwal, Shekhar; Diwakar, Manoj

doi:10.1016/j.ijleo.2020.166007

Cited by 25 publications

(13 citation statements)

References 25 publications

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“…Recognition Rate SVD based VR (2018) [46] 64.40% INNC (2018) [46] 63.60% Naive CR (2020) [47] 64.00% Method based on CR (2020) [47] 74.40% RNLRLSR (2020) [48] 72.00% CLSR (2020) [48] 65.60% DWT(SVD/LR+RWLDA/QR)+MIN-MAX (2019) [49] 89.24% DWT(SVD/LR+RWLDA/QR)+Z-score (2019) [49] 88.20% CMBZZBP (2020) [50] 91.20% DeepWTPCA-L 1 93.89%…”

Section: Methodsmentioning

confidence: 99%

“…We have also compared the performances of the proposed system with 9 state-of-the-art approaches on GTFD database and adopted the same experimental protocol. Table 4 shows the comparison of the recognition rate between our system proposed and these methods including SVD based VR [46], INNC [46], Naive CR [47], Method based on CR [47], RNL-RLSR [48], CLSR [48], DWT(SVD/LR+RWLDA/QR) using MIN-MAX method [49], DWT(SVD/LR+RWLDA/QR) using Z-score method [49], and CMBZZBP [50]. We can…”

Section: Evaluation On Gtfd Datasetmentioning

confidence: 99%

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DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

et al. 2021

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In this paper, we propose a robust face recognition model called DeepWTPCA-L 1 using WTPCA-L 1 features and a CNN-LSTM architecture. First, WTPCA-L 1 algorithm, composed of Threelevel decomposition of discrete wavelet transform followed by PCA-L 1 algorithm, is exploited to extract face features. Then, the extracted features are used as inputs of the proposed CNN-LSTM architecture.To evaluate the robustness of the proposed approach, several face recognition datasets have been used. In addition, the proposed method is trained on noisy images using Gaussian, and Salt & Pepper noise added to the facial images of each dataset. The results of the experiment indicate that the proposed model achieves high recognition performance on three well-known standard face databases. When compared to state-ofthe-art methods, the proposed model achieves a better face recognition rate.

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Section: Methodsmentioning

confidence: 99%

Section: Evaluation On Gtfd Datasetmentioning

confidence: 99%

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

et al. 2021

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“…Therefore, Hala M. Ebied [46] shows that the classical Gaussian-KPCA gives the best face recognition rate with σ = 3000. Recently, the authors [42] have proposed a new solution of Eq (8). This solution is expressed by the decomposition of matrix K using cholesky decomposition K = K c K T c , because the matrix K is symmetry and positive definite.…”

Section: Pca and Kpca Using Rrqr Factorizationmentioning

confidence: 99%

“…However LBP-based technique has some limitations like limited discriminative capacity [7]. However, LBP is used widely for face recogntion like in [8] while the authors used PCA to reduce the features obtained using their two descriptors based on LBP called CZZBP and CMBZZBP. The basic idea of these two descriptors is to exploit the ZigZag features of three RGB components to extract representative information.…”

Section: Introductionmentioning

confidence: 99%

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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“…The local approach considers certain facial features such as Speed-up robust features (SURF) [17], Local Binary Patterns (LBP) [22]. The local information combines with the holistic information to enrich feature descriptors for performance improvements in the hybrid approach: the fusion of 54 Gabor functions and fuzzy logic for facial expression recognition [15], two-color local descriptors, called Color ZigZag Binary Pattern (CZZBP) [19], or a fusion of Deep features [12].…”

Section: Introductionmentioning

confidence: 99%

Weighted statistical binary patterns for facial feature representation

2021

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We present a novel framework for efficient and robust facial feature representation based upon Local Binary Pattern (LBP), called Weighted Statistical Binary Pattern, wherein the descriptors utilize the straight-line topology along with different directions. The input image is initially divided into mean and variance moments. A new variance moment, which contains distinctive facial features, is prepared by extracting root k-th. Then, when Sign and Magnitude components along four different directions using the mean moment are constructed, a weighting approach according to the new variance is applied to each component. Finally, the weighted histograms of Sign and Magnitude components are concatenated to build a novel histogram of Complementary LBP along with different directions. A comprehensive evaluation using six public face datasets suggests that the present framework outperforms the state-of-the-art methods and achieves 98.51% for ORL, 98.72% for YALE, 98.83% for Caltech, 99.52% for AR, 94.78% for FERET, and 99.07% for KDEF in terms of accuracy, respectively. The influence of color spaces and the issue of degraded images are also analyzed with our descriptors. Such a result with theoretical underpinning confirms that our descriptors are robust against noise, illumination variation, diverse facial expressions, and head poses.

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Two novel color local descriptors for face recognition

Cited by 25 publications

References 25 publications

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

DeepWTPCA-L1: A New Deep Face Recognition Model Based on WTPCA-L1 Norm Features

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

Weighted statistical binary patterns for facial feature representation

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