单样本下复杂光照人脸特征提取关键技术研究

项目名称： 单样本下复杂光照人脸特征提取关键技术研究

项目编号： No.61302150

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 无线电电子学、电信技术

项目作者： 高涛

作者单位： 长安大学

项目金额： 24万元

中文摘要： 目前已经提出了许多能够很好解决人脸识别的方法，但是当训练样本为单样本以及光照条件变化时，大多数现有的算法无法达到良好的效果，为了解决实际中存在的单样本和复杂光照问题。该项目提出了一种融合了异构多态全局、局部人脸稀疏特征的单样本特征提取方法，主要研究内容包括复杂光照下人脸图像光照补偿模型、单样本下人脸全局特征的稀疏表征模型、基于局部贡献度的局部特征稀疏表征模型和构建异构多态人脸稀疏特征融合模型的建立。模型将在ORL、YALE、FERET、CMUPIE和YALE B库上做相应的实验室测试和搭建实际外场环境进行实际测试，全面验证模型的有效性、光照鲁棒性、遮挡鲁棒性、实时性等性能。本项目是在前人研究人脸特征的自然深入和发展，解决了现有方法的诸多不足，具有明显的创新性，不仅对图像的复杂光照处理、稀疏表征、特征级融合理论有学术价值，并且对公共场所安全布控、金融安全等领域产生良好的经济效益和社会效益。

中文关键词： 特征提取；人脸识别；复杂光照；单样本；图像表征

英文摘要： At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only single example image per person and there is under complex illumination. In order to deal with this problem of single example image per person and complex illumination stored in the real-world application, the project proposed a novel comprehensive feature extraction fusion model using global and local feature sparse representation. The main research content of this project includes: illumination compensation model with single training sample under complex illumination,global feature sparse representation model with single training sample, local feature sparse representation model based on local image contribution degree map with single training sample, polymorphic and isomeric face sparse representation fusion model. Laboratory experiments and field experiments will be implemented on YALE,ORL,FERET,CMUPIE and YALE B face databases and the to demonstrate the efficient, complex illumination robustness, partial occlusion robustness, real time capability of proposed methods. This project is natural depth and development base on the previous face feature extraction research. It can solve many insufficiencies of existent methods and has strong creativity. It not only has

英文关键词： feature extraction；face recognition；complex illumination；single sample；image representation