面向生物特征识别的鲁棒判别结构化特征表示方法研究

项目名称： 面向生物特征识别的鲁棒判别结构化特征表示方法研究

项目编号： No.61502245

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 高广谓

作者单位： 南京邮电大学

项目金额： 21万元

中文摘要： 特征抽取是模式识别和计算机视觉领域的经典研究课题，研究该课题的关键任务之一是设计判别能力强、对表现变化鲁棒的图像特征表示方法。针对这一任务，本项目结合稀疏表示和低秩矩阵恢复等理论，研究面向生物特征识别的特征表示方法，丰富和发展特征抽取理论体系。具体的研究内容包括：（1）设计鲁棒加权表示学习方法，使得抽取的特征更有益于识别；（2）研究性能优越的特征生成方法，增强生成特征的鲁棒性；（3）探索多方向特征表示方法，保持图像结构信息和多方向信息；（4）开发具备鲁棒性和判别性的特征抽取算法，搭建一个融鲁棒表示与判别特征抽取于一体的框架。本项目的预期成果是：在深入分析当前表示学习方法和充分大规模试验的基础上，提出具备较强鲁棒性和判别能力的特征生成和特征抽取方法，提高计算机对图像的理解和感知能力；建立鲁棒特征表示与判别特征抽取的一体化理论与算法框架，使得整个生物特征识别系统达到最佳性能。

中文关键词： 特征抽取；特征表示；稀疏表示；低秩表示；生物特征识别

英文摘要： Feature extraction has been a classical research topic in the field of pattern recognition and computer vision. A critical problem in this research topic is to design image feature representation methods which are both discriminative and robust to appearance variations. This project would investigate the feature representation methods for biometric recognition by integrating sparse representation and low-rank matrix recovery theory, to enrich and develop the theoretical system of feature extraction. Particularly, the points of our research mainly include: (1) Designing robust weighted representation learning methods, making the extracted features beneficial to recognition tasks; (2) Studying feature generation methods, which has superior performance, strengthening the robustness of the generated features; (3) Exploring multiple orientation feature representation methods, keeping the image structure and multiple orientation; (4) Developing robust and discriminative feature extraction methods, building a model to unify robust representation and discriminative feature extraction. The main expected contributions of the project are: proposing robust and discriminative feature generation and feature extraction methods to improve the understanding and perception of computer, on basis of the in-depth analysis on the current representation learning methods; building a framework to unify robust feature representation and discriminative feature extraction theory and algorithms to make the biometric recognition system achieve the optimal performance.

英文关键词： feature extraction;feature representation;sparse representation;low-rank representation;biometric recognition