基于加权多尺度张量子空间的人脸图像特征提取方法

Method for Face Image Feature Extraction Based on Weighted Multi-Scale Tensor Subspace

为了不破坏原始数据固有的高阶结构和数据之间的相关性,减少光照对图像特征的影响,并优化多尺度特征的权重,提出了基于加权多尺度张量子空间的图像特征提取方法。采用多尺度小波变换表征图像各个部位特征,使用不确定度权衡每个尺度对图像分类的作用,并组建成多尺度张量子空间,结合多线性主成分分析与线性判别分析算法,降低了图像在处理过程中的成本,保存了高维数据固有结构和相关性,完成对图像特征提取。使用CAS-PEAL-R1东方人脸库进行评测,实验结果表明,该图像特征提取算法用于图像识别过程中具有较好的效果,具有一定的可行性。

In order to keep the inherent higher order structure and correlation in the original data, reduce the influence of illumination in image recognition, and optimize the weight of the multi-scale feature, the method of image feature extracting based on weighted multi-scale tensor subspace is proposed to solve the problems. Firstly, multi-scale transform is used to characterize each place feature of the image, and un- certainty weighed is adopted on the role of each scale feature for image classification. And then a multiscale tensor space is built using multiple linear principal component analysis and linear discriminant analy- sis algorithm to reduce the cost of processing, preserving the inherent structure and correlation of high- dimensional data. Finally, the extraction of the image features is completed. CAS-PEAL-R1 oriental face database is chosen for evaluation. The experimental results show that the algorithm performs better than some recent algorithms for image recognition with practical feasibility.