...准确地估计训练图像的协方差矩阵等问题,提出了一种基于描述特征的人脸识别算法(Expressive feature face recognition algorithm,EFFRA)．该算法用训练图像的右奇异向量代替PCA求解的子空间的基向量,避免了将人脸图像转换成图像向量,明显降...
基于2个网页-相关网页
基于核主成分分析（KPCA）的人脸识别算法能够提取非线性图像特征，在小样本训练条件下有较好性能。
The algorithm of face recognition based on kernel principal component analysis(KPCA)can abstract nonlinear features of image and can get better performance under less sample training conditions.
并在特征提取环节，提出CSVD算法与非负矩阵因子算法特征数据相融合的人脸识别算法。
And in the feature extraction process, a new face recognition method based on CSVD and non Negative Matrix Factorization (NMF) is presented.
眼角的自动定位能够给后续的人脸特征自动提取和识别算法研究奠定良好的基础，帮助提高人脸识别算法的识别率。
This approach would help to extract the vital feature points on human face automatically and improve the accuracy of face recognition.
应用推荐