F-2D-QPCA: A Quaternion Principal Component Analysis Method for Color Face Recognition

Wang, Minghui; Song, Lili; Sun, Kaisong; Jia, Zhigang

doi:10.1109/access.2020.3041847

Cited by 9 publications

(5 citation statements)

References 42 publications

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“…Due to the competitive performance of quaternion-based PCA and the theoretical guarantee, there are many related works [75]. For example, Wang et al [76] proposed a robust subspace learning method with PCA for face recognition under the Georgia Tech face dataset (https://computervisiononline.com/dataset/1105138700, accessed on 1 March 2023) and the color FERET (https://www.nist.gov/itl/iad/imagegroup/color-feret-database, accessed on 1 March 2023) dataset. Sun et al [77] suggested modified two-dimensional principal component analysis (2DPCA) and bidirectional principal component analysis (BDPCA) methods based on the quaternion matrix to recognize and reconstruct face images.…”

Section: Low-rank-based and Sparse-based Modelsmentioning

confidence: 99%

Review of Quaternion-Based Color Image Processing Methods

Huang

Gao

2023

Mathematics

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Images are a convenient way for humans to obtain information and knowledge, but they are often destroyed throughout the collection or distribution process. Therefore, image processing evolves as the need arises, and color image processing is a broad and active field. A color image includes three distinct but closely related channels (red, green, and blue (RGB)). Compared to directly expressing color images as vectors or matrices, the quaternion representation offers an effective alternative. There are several papers and works on this subject, as well as numerous definitions, hypotheses, and methodologies. Our observations indicate that the quaternion representation method is effective, and models and methods based on it have rapidly developed. Hence, the purpose of this paper is to review and categorize past methods, as well as study their efficacy and computational examples. We hope that this research will be helpful to academics interested in quaternion representation.

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Section: Low-rank-based and Sparse-based Modelsmentioning

confidence: 99%

Review of Quaternion-Based Color Image Processing Methods

Huang

Gao

2023

Mathematics

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“…Xiao and Zhou [48] proposed a novel quaternion ridge regression model for 2D-QPCA and proposed the QRR-2D-QPCA. Wang et al [49] replaced square F-norm with F-norm in 2D-QPCA and proposed the F-2D-QPCA. Jia et al [50] replaced 2 -norm with p -norm in 2D-QPCA and proposed the generalized 2D-QPCA (G-2D-QPCA).…”

Section: Related Workmentioning

confidence: 99%

“…Bilateral algorithms Zhang et al [21] (2D) 2 PCA Kong et al [22] Generalized 2DPCA Yang et al [23] RC2DPCA Subimages Titijaroonroj et al [24] RCM-2DPCA Sahoo et al [25] ESIMPCA, EFLPCA Sahoo et al [26] Bi-ESIMPCA, Bi-EFLPCA Robust and sparse modelling Li et al [27] 2DPCA-L1 Wang et al [28] non-greedy 2DPCA-L1 Yang et al [29] 2DPCA-T 1 Wang and Wang [30] 2DPCAL1-S Wang [31] G2DPCA Rotational invariance Gao et al [34] R 1 -2DPCA Li et al [35] F-2DPCA Gao et al [36] Angle-2DPCA Wang and Li [37] Area-2DPCA Wang et al [38] Cos-2DPCA Bi et al [39] ROMCA-2DPCA Razzak et al [40] ORPCA Mi et al [41] 2,p -2DPCA Zhou et al [42] GC-2DPCA Kuang et al [43] F p -2DPCA Zhou et al [44] BA2DPCA Bi et al [45] 2,p -SB-2DPCA Color image processing Xiang et al [46] C-2DPCA Jia et al [47] 2D-QPCA Xiao and Zhou [48] QRR-2D-QPCA Wang et al [49] F-2D-QPCA Jia et al [50] G-2D-QPCA Zhao et al [ 2DPCA-Net Li et al [57] L1-2DPCA-Net…”

Section: Category Bibliography Algorithmmentioning

confidence: 99%

Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

et al. 2022

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Being an efficient image reconstruction and recognition algorithm, two-dimensional PCA (2DPCA) has an obvious disadvantage in that it treats the rows and columns of images unequally. To exploit the other lateral information of images, alternative 2DPCA (A2DPCA) and a series of bilateral 2DPCA algorithms have been proposed. This paper proposes a new algorithm named direct bilateral 2DPCA (DB2DPCA) by fusing bilateral information from images directly—that is, we concatenate the projection results of 2DPCA and A2DPCA together as the projection result of DB2DPCA and we average between the reconstruction results of 2DPCA and A2DPCA as the reconstruction result of DB2DPCA. The relationships between DB2DPCA and related algorithms are discussed under some extreme conditions when images are reshaped. To test the proposed algorithm, we conduct experiments of image reconstruction and recognition on two face databases, a handwritten character database and a palmprint database. The performances of different algorithms are evaluated by reconstruction errors and classification accuracies. Experimental results show that DB2DPCA generally outperforms competing algorithms both in image reconstruction and recognition. Additional experiments on reordered and reshaped databases further demonstrate the superiority of the proposed algorithm. In conclusion, DB2DPCA is a rather simple but highly effective algorithm for image reconstruction and recognition.

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“…PCA has been found used in a wide range of fields ranging from spike-triggered covariance analysis in neuroscience [49], [50], to quantitative finance [51]- [54] with the most common application being facial recognition [54]- [56] and other applications like medical data correlation [57]- [60].…”

Section: Principal Component Analysismentioning

confidence: 99%

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

Hasan

Abdulazeez

2021

JSCDM

123

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Big databases are increasingly widespread and are therefore hard to understand, in exploratory biomedicine science, big data in health research is highly exciting because data-based analyses can travel quicker than hypothesis-based research. Principal Component Analysis (PCA) is a method to reduce the dimensionality of certain datasets. Improves interpretability but without losing much information. It achieves this by creating new covariates that are not related to each other. Finding those new variables, or what we call the main components, will reduce the eigenvalue /eigenvectors solution problem. (PCA) can be said to be an adaptive data analysis technology because technology variables are developed to adapt to different data types and structures. This review will start by introducing the basic ideas of (PCA), describe some concepts related to (PCA), and discussing. What it can do, and reviewed fifteen articles of (PCA) that have been introduced and published in the last three years.

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F-2D-QPCA: A Quaternion Principal Component Analysis Method for Color Face Recognition

Cited by 9 publications

References 42 publications

Review of Quaternion-Based Color Image Processing Methods

Review of Quaternion-Based Color Image Processing Methods

Fusion of Bilateral 2DPCA Information for Image Reconstruction and Recognition

A Review of Principal Component Analysis Algorithm for Dimensionality Reduction

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