Fusion of Global and Local Gaussian-Hermite Moments for Face Recognition

Song, Guojie; He, Dan; Chen, Puchun; Tian, Jing; Zhou, Bin; Luo, Ling

doi:10.1007/978-981-13-9917-6_17

Cited by 6 publications

(9 citation statements)

References 29 publications

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“…The recognition rate of the LWKPCA method are compared with several recent advanced face recognition methods. Table 6 displays the recognition rate of our proposed approach compared with several recent face recognition methods including DIWTLBP [56], CFLDA [57], WTPCA-L1 [52], Gabor+SRC [58], LDA+SRC [58], DWT(SVD/LR+RWLDA/QR) [29], RKPCA [42], 2D-DMWT [59], IKLDA+PNN [60], DCT+VQ [61], Ga-bor+FastICA+LDA [31]. The proposed method adopts the same experimental-protocol of these methods.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

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

et al. 2022

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“…Different OMs have been used in this field, such as higher-order OMs [32], Fourier-Mellin moments [33], rotation-invariant complex Zernike moments [34], discrete Krawtchouk moments [35], Tchebichef moments [36], orthogonal exponent Fourier moments [37], 2D orthogonal Gaussian-Hermite moments [38], and 2D Krawtchouk moments [21]. The 2D Krawtchouk OMs provided good results in conditions with noise, tilt, and changes in expression [39]. In comparison with other moments, Gaussian-Hermite moments are considered very robust against noise [28,40].…”

Section: Literature Review and Discussionmentioning

confidence: 99%

“…In comparison with other moments, Gaussian-Hermite moments are considered very robust against noise [28,40]. Gaussian-Hermite moments can bne used as a set of useful features to capture the facial expression from face images [39,41]. Generally, the extraction methods of image features are classified into two groups: global features-based methods (termed Holistic approaches [42]) and local features-based methods (termed Component-based methods [42] or Block Processingbased methods).…”

Section: Literature Review and Discussionmentioning

confidence: 99%

“…Generally, the extraction methods of image features are classified into two groups: global features-based methods (termed Holistic approaches [42]) and local features-based methods (termed Component-based methods [42] or Block Processingbased methods). The former method captures the features from an entire image of a human face, while the local feature extraction method can extract features from certain areas of the face image, such as the eyes, mouth, and chin [39]. There are various global feature extraction methods, such as Eigenfaces [43], Fisher faces [44], Linear Discriminant Analysis [45], Discrete Cosine Transform [46], Independent Component Analysis [47], and others.…”

Section: Literature Review and Discussionmentioning

confidence: 99%

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Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

et al. 2022

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Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face image datasets, ORL and FEI. Different state-of-the-art face recognition methods were compared with the proposed method in order to evaluate its accuracy. We demonstrate that the proposed method achieves the highest recognition rate in different considered scenarios. Based on the obtained results, it can be seen that the proposed method is robust against noise and significantly outperforms previous approaches in terms of speed.

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“…The former is also called a holistic-based approach [ 63 ], which can capture the essential characteristics of the full human face image. At the same time, it is known as the component-based approach or block-processing-based approach, from specific areas in images [ 64 ]. In the global feature-based approach, various imaging setups are used to achieve improved performance for feature extraction [ 65 ].…”

Section: Introductionmentioning

confidence: 99%

3D Object Recognition Using Fast Overlapped Block Processing Technique

Mahmmod

Abdulhussain

Naser

et al. 2022

Sensors

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Three-dimensional (3D) image and medical image processing, which are considered big data analysis, have attracted significant attention during the last few years. To this end, efficient 3D object recognition techniques could be beneficial to such image and medical image processing. However, to date, most of the proposed methods for 3D object recognition experience major challenges in terms of high computational complexity. This is attributed to the fact that the computational complexity and execution time are increased when the dimensions of the object are increased, which is the case in 3D object recognition. Therefore, finding an efficient method for obtaining high recognition accuracy with low computational complexity is essential. To this end, this paper presents an efficient method for 3D object recognition with low computational complexity. Specifically, the proposed method uses a fast overlapped technique, which deals with higher-order polynomials and high-dimensional objects. The fast overlapped block-processing algorithm reduces the computational complexity of feature extraction. This paper also exploits Charlier polynomials and their moments along with support vector machine (SVM). The evaluation of the presented method is carried out using a well-known dataset, the McGill benchmark dataset. Besides, comparisons are performed with existing 3D object recognition methods. The results show that the proposed 3D object recognition approach achieves high recognition rates under different noisy environments. Furthermore, the results show that the presented method has the potential to mitigate noise distortion and outperforms existing methods in terms of computation time under noise-free and different noisy environments.

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Fusion of Global and Local Gaussian-Hermite Moments for Face Recognition

Cited by 6 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

Face Recognition Algorithm Based on Fast Computation of Orthogonal Moments

3D Object Recognition Using Fast Overlapped Block Processing Technique

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