学术报告

【online】Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition

发布人:发布时间: 2021-11-05

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题目:Advanced Variations of Two-Dimensional Principal Component Analysis for Face Recognition

 

报告人:贾志刚 教授(江苏师范大学)


时间:20211110日(星期三)10:00 


报告方式:腾讯会议  ID号:323 883 941 


摘要: The two-dimensional principal component analysis (2DPCA) has been one of the basic methods of developing artificial intelligent algorithms. To increase the feasibility, we propose a new general ridge regression model for 2DPCA and variations, with extracting low dimensional features from two projection subspaces. A new relaxed 2DPCA under the quaternion framework is proposed to utilize the label (if known) and color information to compute the essential features of generalization ability with optimization algorithms. The 2DPCA-based approaches for face recognition are also improved by weighting each principle component a scatter measure, which increases efficiently the rate of face recognition. In numerical experiments on well-known standard databases, the R2DPCA approach has high generalization ability and achieves a higher recognition rate than the state-of-the-art 2DPCA-like methods, and has better performance than the basic deep learning methods such as CNNs, DBNs, and DNNs in the small-sample case.

 

报告人简介:贾志刚 ,江苏师范大学教授、硕士生导师。2009年毕业于华东师范大学数学系,获理学博士学位。主要研究方向为数值代数与图像处理,至今已在SIAM J. Matrix Anal. Appl., SIAM J. Sci. Comput., SIAM J. Imaging Sci., J. Sci. Comput., Numer. Linear Algebra Appl.等国际知名期刊上发表学术论文40余篇,在科学出版社出版专著和译著各1部,主持国家自然科学基金项目3项、省高校自然科学研究重大项目1项,参加国家自然科学基金重大项目1项。先后入选江苏师范大学“第一批高层次人才队伍后备人选”、“三育人先进个人”、“校先进工作者”等。现兼职为中国高等教育学会教育数学专业委员会常务理事、江苏省计算数学学会理事、美国Math Review评论员等。


邀请人:郭朕臣 老师