Quasi Non-Negative Quaternion Matrix Factorization with Application to Color Face Recognition
This work addresses color image processing challenges for applications like face recognition, but it is incremental as it builds on existing quaternion and matrix factorization methods.
The paper tackles the non-negativity dropout problem in quaternion models by proposing a quasi non-negative quaternion matrix factorization (QNQMF) model for color image processing, resulting in better performance in color image reconstruction and face recognition compared to RGB or gray-level channels, with improved accuracy rates under variations in facial expressions and shooting angles.
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.