一种应用于人脸识别的超分辨率方法

为提高局部保持投影(LPP)在人脸图像超分辨率中的适用性,在LPP中引入典型相关分析(CCA),提出一种相关性增强的局部保持投影方法(CELPP)。CELPP用于提取高分辨率图像与低分辨率图像特征,根据关系学习建立低分辨率图像特征与高分辨率图像特征之间的映射变换,输入低分辨率图像,通过CELPP特征提取和关系映射,得到高分辨率图像,并将其用于人脸识别。对人脸库ORL和Yale进行的实验结果表明,该方法同时考虑了高分辨率图像与低分辨率图像的相似性及同类图像的局部结构性,在基于人脸识别的超分辨率应用中优于LPP和CCA。

A Super-resolution Method Applied to Face Recognition

To enhance the applicability of Locality Preserving Projections（LPP） in super-resolution of face images, this paper proposes an improved method, Correlation Enhanced Locality Preserving Projection（CELPP）, which introduces the method of Canonical Correlation Analyses（CCA） into LPE CELPP is used for feature extraction, and relationship learning is used to build a bridge for the transformation of high resolution images and low resolution images. Entering low resolution images, through CELPP feature extraction and mapping transformation, the high resolution imageg are achieved and used for face recognition. Experimental results of ORL and Yale databases show that CELPP is better than LPP and CCA in super-resolution applications because CELPP considers the similarity of high resolution images and low resolution images, and the local structure of the same class images.