论文标题
准非负季基矩阵分解，并应用于彩色面部识别
Quasi Non-Negative Quaternion Matrix Factorization with Application to Color Face Recognition
论文作者
论文摘要
为了解决四个季学模型的非负辍学问题，提出了一种新型的准定量Quaternion矩阵分解（QNQMF）模型，用于颜色图像处理。为了实施QNQMF，通过将QNQMF作为非convex约束季节优化问题提出了乘数的四元素预测的梯度算法和四个方向方法的交流方向方法。研究了所提出的算法的某些特性。颜色图像重建上的数值实验表明，这些算法在季节上编码的算法比在红色，绿色和蓝色通道上编码的这些算法更好。此外，我们将提出的算法应用于颜色的面部识别。数值结果表明，当出现大型面部表情和射击角变化时，五元模型上面部识别的准确率和颜色图像的红色，绿色和蓝色通道以及灰度图像的单个通道的精度更好。
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.
