一种改进的基于NMF的人脸识别方法

Improved Face Recognition Method Based on NMF

针对NMF(非负矩阵分解)算法基于局部特征提取的特点,提出了一种对NMF基矩阵的处理方法,以提高在局部遮挡环境下人脸识别系统的识别率。首先使用离散小波变换得到样本的低频信息,利用NMF得到基矩阵;然后通过阈值判断提取能够突出表现人脸特征的部分,得到优化后的特征子空间,并将样本在该子空间上投影;最后使用支持向量机对所得到的投影系数分类。实验结果表明,优化算法其运算时间较短,且能有效地提高人脸在部分遮挡环境中的识别率。

Non-negative matrix factorization（NMF） is a method of parts-based feature extraction,according to this characteristic.An improved algorithm dealing with NMF basis was introduced to enhance partial occlusion face recognition rate.Firstly,discrete wavelet transformation was used to produce a representation in the low frequency domain,and basic matrix was got according to the NMF method.Secondly,parts of face features which possess outstanding perfor-mance were extracted by threshold value judgments,and they were used to form optimized facial subspace feature.The training and testing images were projected to the optimized subspace feature.Finally support vector machine was used for classification.Experiments show that the improved algorithm＇s computing time is short,and this method achieves remarkable effects under the partially occluded circumstance.