MiLDA: A graph embedding approach to multi-view face recognition
Section snippets
Canonical correlation analysis
The most famous subspace-based multi-view data analysis approach is canonical correlation analysis (CCA) [26], the goal of which is to extract pairs of directions that maximize correlation between two data sets.
Formally, given two matrices in which and in which , CCA iteratively learns the pairs of normalized projection directions uncorrelated with the pairs previously learned. The first pair of projection directions v1 and v2
The proposed graph embedding framework for multi-view data analysis and MiLDA
In this section, we present our graph embedding framework for multi-view data analysis and MiLDA. First of all, we briefly review the graph embedding formulation, which unifies various classical dimensionality reduction approaches. We then introduce our graph embedding framework and elaborate its solution, and finally propose MiLDA on the basis of the framework.
Experiments
As we have mentioned, a big challenge in multi-view face recognition is due to the significant difference between face images captured in different scenarios. To empirically demonstrate the ability of MiLDA to overcome this challenge, here we conduct experiments for two multi-view face recognition scenarios, one is the multi-view face recognition across pose and illumination, and the other is the still-to-video face recognition.
Conclusions
In this paper, we have presented a graph embedding framework for multi-view data analysis. Our framework projects multi-view data into a common subspace of higher discriminability. By utilizing this framework, we have also proposed multi-view linear discriminant analysis (MiLDA) for multi-view face recognition. MiLDA can be solved efficiently, and has demonstrated superior empirical performance to some popular approaches. Future work will focus on its further improvement by combining it with
Acknowledgements
We thank the three anonymous reviewers for their constructive comments, in particular those on the relationship between MiLDA, MvDA and GMLDA. This work was partially supported by the National Basic Research Program of China 973 program under Grant no. 2013CB329403.
Yiwen Guo received the B.E. degree from Wuhan University, China, in 2011. He is currently working toward the Ph.D. degree with the Department of Electronic Engineering in Tsinghua University, China. His current research interests include computer vision, pattern recognition, machine learning and face recognition.
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Yiwen Guo received the B.E. degree from Wuhan University, China, in 2011. He is currently working toward the Ph.D. degree with the Department of Electronic Engineering in Tsinghua University, China. His current research interests include computer vision, pattern recognition, machine learning and face recognition.
Xiaoqing Ding received the B.E. degree from Tsinghua University, China, in 1962. She is currently a Professor and a Ph.D. Supervisor with the Department of Electronic Engineering, Tsinghua University. Her research interests include computer vision, pattern recognition, machine learning, image processing, face recognition, character recognition, etc. She was a recipient of a series of achievements on Chinese/multilanguage character recognition, face recognition, etc. She was a recipient of the most prestigious National Scientific and Technical Progress Awards in China in 1992, 1998, 2003, and 2008.
Jing-Hao Xue received the B.Eng. degree in telecommunication and information systems in 1993 and the Dr.Eng. degree in signal and information processing in 1998, both from Tsinghua University, the M.Sc. degree in medical imaging and the M.Sc. degree in statistics, both from Katholieke Universiteit Leuven in 2004, and the degree of Ph.D. in statistics from the University of Glasgow in 2008. He has worked in the Department of Statistical Science at University College London as a Lecturer since 2008. His research interests include statistical and machine-learning techniques for pattern recognition, data mining and image processing, in particular supervised, unsupervised and incompletely-supervised learning for complex and high-dimensional data.