摘要
为了获取更充分的人脸特征信息以提高识别性能,应用加权小波变换和流形正则化非负矩阵分解的方法实现人脸识别。采用小波变换,提取训练样本人脸图像的加权高频分量和低频分量的特征信息;应用流形正则化非负矩阵分解方法,在保持人脸特征数据原始几何结构和局部特征的基础上获取最终的识别特征;利用最近邻方法进行分类识别。将该算法在ORL人脸库和YALE人脸库上进行测试验证,结果表明,与传统的非负矩阵分解方法相比,其识别率高出5%左右,且计算时间很低,说明该方法耗时短,效率高。
In order to improve the recognition performance by obtaining more sufficient face features, the method of weighted wavelet decomposition and manifold regularized non-negative matrix factorization is introduced to realize face recognition. Firstly, wavelet decomposition with its weighted high frequency is applied to extract the features of weighted high frequency component and low frequency component from training samples. Secondly, with maintaining potential geometric structures and local features of the face features, it uses manifold regularized non-negative matrix factorization to acquire final recognition characteristics. Lastly, nearest neighbor method is used to be classified and recognized. Comparing with the traditional method of non-negative matrix factorization, experimental results on ORL face databases and YALE face databases show that the recognition rate is about increased by 5% and computation time is quite shorter. Hence, the proposed method has less time consuming, as well as a better recognition performance.
出处
《计算机工程与应用》
CSCD
北大核心
2016年第7期150-154,190,共6页
Computer Engineering and Applications
基金
国家自然科学基金(No.61301276)
西安工程大学控制科学与工程学科建设经费资助(No.107090811)
西安工程大学博士科研启动金项目(No.BS1207)
陕西省级大学生创新创业训练计划项目(No.1571)
关键词
人脸识别
加权小波变换
非负矩阵分解
流形正则化
face recognition
weighted wavelet decomposition
non-negative matrix factorization
manifold regularization
