基于三维模型子空间监督学习法的二维人脸识别

人脸识别的一个主要难点在于人脸姿态和光照变化对识别性能影响显著.考虑到此问题,本文提出了一种将三维模型和二维照片相结合的新的人脸识别技术,对不同人脸姿态和光照变化有很好的鲁棒性.在训练阶段由特定三维人脸模型生成大量带有不同姿态和光照的虚拟二维照片,采用监督学习法使这些虚拟照片形成子空间,最终组成特定人脸模板.此时不再需要三维数据,只要匹配真实二维照片和模板就可以进行人脸识别.

2D Face Recognition Based on Supervised Subspace Learning From 3D Models

One of the main challenges in face recognition is represented by pose and illumination variations that drastically affect the recognition performance.This paper presents a new technique for face recognition,based on the joint use of 3D models and 2D images,specifically conceived to be robust with respect to pose and illumination changes.A 3D model of each user is exploited in the training stage to generate a large number of 2D images representing virtual views of the face with varying pose and illumination....