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Journal of Scientific Computing

Erschienen in:

01.05.2023

Quasi Non-Negative Quaternion Matrix Factorization with Application to Color Face Recognition

verfasst von: Yifen Ke, Changfeng Ma, Zhigang Jia, Yajun Xie, Riwei Liao

Erschienen in: Journal of Scientific Computing | Ausgabe 2/2023

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Abstract

To address the non-negativity dropout problem of quaternion models, a novel quasi non-negative quaternion matrix factorization (QNQMF) model is presented for color image processing. To implement QNQMF, the quaternion projected gradient algorithm and the quaternion alternating direction method of multipliers are proposed via formulating QNQMF as the non-convex constraint quaternion optimization problems. Some properties of the proposed algorithms are studied. The numerical experiments on the color image reconstruction show that these algorithms encoded on the quaternion perform better than these algorithms encoded on the red, green and blue channels. Furthermore, we apply the proposed algorithms to the color face recognition. Numerical results indicate that the accuracy rate of face recognition on the quaternion model is better than on the red, green and blue channels of color image as well as single channel of gray level images for the same data, when large facial expressions and shooting angle variations are presented.

Vorheriger Artikel A Positivity-Preserving, Energy Stable BDF2 Scheme with Variable Steps for the Cahn–Hilliard Equation with Logarithmic Potential

Nächster Artikel Proximal Quasi-Newton Method for Composite Optimization over the Stiefel Manifold

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Titel: Quasi Non-Negative Quaternion Matrix Factorization with Application to Color Face Recognition
verfasst von: Yifen Ke
Changfeng Ma
Zhigang Jia
Yajun Xie
Riwei Liao
Publikationsdatum: 01.05.2023
Verlag: Springer US
Erschienen in: Journal of Scientific Computing / Ausgabe 2/2023
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI: https://doi.org/10.1007/s10915-023-02157-x

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