姿态和光照可变条件下的仿射最小线性重构误差人脸识别算法

传统人脸识别算法通常把光照处理和姿态校正作为两个相对独立的处理过程,难以取得全局最优识别性能.针对该问题,本文根据人脸的非刚体特性,将仿射变换和分块思想融入线性重构模型中,提出了一种基于仿射最小线性重构误差(Affine Minimum Linear Reconstruction Error,AMLRE)的人脸识别算法,在处理光照问题的同时能够补偿姿态变化造成的局部区域对齐误差,以获得更好的全局识别性能.在公共数据集上的实验结果表明,本文提出的算法对光照和姿态有很好的鲁棒性,同时与现有的人脸识别算法相比,本文的算法具有更高的识别率.

Affine Minimum Linear Reconstruction Error Face Recognition Under Varying Pose and Illumination

Traditional face recognition algorithms usually handle variations in illumination and pose independently.Therefore,it is difficult to obtain the global optimal recognition performance.To this end,we propose an affine minimum linear reconstruction error (AMLRE) algorithm based on the non-rigid characteristics of human faces in this paper,which combines an affine transformation model and the idea of patch with a linear reconstruction model. Our algorithm simultaneously handles illumination variations as well as compensates the local area alignment errors caused by pose variations,which achieves much better recognition performance.Comprehensive experiments on several public face datasets clearly demonstrate that our proposed algorithm is robust to both illumination and pose,and thus outperforms most state-of-the-art methods.