Kernelised orthonormal random projection on grassmann manifolds with applications to action and gait-based gender recognition

Zhao, Kun, Wiliem, Arnold and Lovell, Brian (2015). Kernelised orthonormal random projection on grassmann manifolds with applications to action and gait-based gender recognition. In: 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015. 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015, Hong Kong, (). March 23-25, 2015. doi:10.1109/ISBA.2015.7126348


Author Zhao, Kun
Wiliem, Arnold
Lovell, Brian
Title of paper Kernelised orthonormal random projection on grassmann manifolds with applications to action and gait-based gender recognition
Conference name 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015
Conference location Hong Kong
Conference dates March 23-25, 2015
Convener IEEE
Proceedings title 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015
Journal name 2015 IEEE International Conference on Identity, Security and Behavior Analysis, ISBA 2015
Place of Publication Piscataway, United States
Publisher Institute of Electrical and Electronics Engineers
Publication Year 2015
Sub-type Fully published paper
DOI 10.1109/ISBA.2015.7126348
ISBN 9781479919741
Total pages 6
Collection year 2016
Language eng
Formatted Abstract/Summary
Video surveillance systems require both accurate and efficient operations for biometric classification tasks. Recent research has shown that modelling video data on manifold space leads to significant improvement on classification accuracy. However, processing manifold points directly often requires computationally expensive operations since manifolds are non-Euclidean. In this work, we tackle this problem by projecting the manifold points into a random projection space constructed by orthonormal hyperplanes. As the projection notion in manifold space is generally not well defined, the random projection is done indirectly via the Reproducing Kernel Hilbert Space (RKHS). There are at least two reasons that make random projection for manifold points attractive: (1) by random projection, manifold points can be projected into lower dimensional space while preserving most of the structure in the RKHS; and (2) after random projection, the classification of manifold points can be solved via scalable linear classifiers. Our formulation is novel compared to the previous work in the way that we use an orthogonality constraint in the hyperplane generation. By orthogonalising the hyperplanes, the mutual information between the dimensions in the projected space is maximised; a desirable property for addressing classification problems. Experimental results in two biometric applications such as action and gait-based gender recognition, show that we can achieve better accuracy than the state-of-the-art random projection method for manifold points. Further, comparisons with kernelised classifiers show that our method achieves nearly 3-fold speed up on average whilst maintaining the accuracy.
Subjects 1305 Biotechnology
1701 Psychology
1706 Computer Science Applications
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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