改进的Gabor变换和二维NMF融合的人脸识别

Face recognition based on improved Gabor transform and nonnegative matrix factorization

为了得到高质量的人脸特征,提高人脸识别性能,提出基于改进的Gabor变换和(2D)2NMF(二维非负矩阵分解法)的人脸识别方法。改进的Gabor变换提取的特征有较高的品质,鲁棒性增强。二维非负矩阵分解法降维能大大降低图像数据维数,缩短计算时间,提高识别率。最后在ORL人脸库中进行实验,结果表明改进的Gabor变换和二维NMF方法相结合计算时间略微增加,但识别效率明显提高,从而证明了该方法的有效性。

In order to get the high quality facial features and improve the performance of face recognition, the face recognition method based on improved Gabor transform and two-dimensional non negative matrix factorization is proposed in this paper. Improved Gabor transform extracts the characteristics with a higher quality, enhances the robustness. Two dimensional non negative matrix decomposition of dimensionality reduction can greatly reduce the dimension of the image data, shorten the calculation time, improve the recognition rate. At last, experiments are carried in the ORL face database. The results show that improved Gabor transform and two-dimensional NMF method have a slight increase in the calculation time, but the recognition efficiency is improved obviously, thus proves the effectiveness of this method.