松弛耦合非负矩阵分解的低分辨率人脸识别算法

Low Resolution Face Recognition Algorithm based on Relaxed Coupled Nonnegative Matrix Factorization

  • 摘要: 针对实际监控场景中经常遇到的人脸图像分辨率较低的问题,本文提出了一种利用耦合非负矩阵分解并保持系数松弛的低分辨率人脸识别算法(Relaxed Coupled Nonnegative Matrix Factorization,后文简称RCNMF)。首先,对高低分辨率人脸图像进行非负矩阵矩阵分解(nonnegative matrix factorization,后文简称NMF),在分解的同时保持组合系数近似一致,从而得到高低分辨率图像的基矩阵。然后,通过低分辨率图像的基矩阵提取训练和测试样本的特征。最后进行识别。实验结果验证了与其他几种基于耦合映射的低分辨率人脸识别方法相比,RCNMF算法的识别性能更好。同时通过实验验证了RCNMF算法的收敛性。

     

    Abstract: In order to solve the problem of low resolution of face image, this paper proposes a algorithm for low resolution face image recognition which utlize the coupled nonnegative matrix factorization and maintains the coefficient relaxation (RCNMF). First, the nonnegative matrix factorization (NMF) is performed for high-resolution and low-resolution face images. Secondly, the combination coefficients are kept approximately the same while decomposing, so as to obtain the base matrices of high-resolution and low-resolution images. Finally, the features of training and test samples are extracted by the basis matrix of low resolution images for recognition. Our experiments verify that the RCNMF algorithm is more effective to solve low resolution face recognition problem than the other state-of-the-art methods based on coupled mapping. At the same time, the convergence of the proposed RCNMF algorithm is verified by experiments.

     

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