Set-to-set gait recognition across varying views and walking conditions

Authors:
Nini Liu;Jiwen Lu;Yap-Peng Tan; Maodong Li
Affiliations:
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore;School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Venue:
ICME '11 Proceedings of the 2011 IEEE International Conference on Multimedia and Expo
Year:
2011

Citing 0
Cited 2

Grassmannian locality preserving discriminant analysis to view invariant gait recognition with image sets

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Robust gait recognition via discriminative set matching

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Abstract

This paper examines the multiview gait recognition problem in which human gait sequences are collected from several different views simultaneously. Motivated by the fact that set-based feature representation can handle certain intra-subject variations, we propose a new Multiview Subspace Representation (MSR) method for gait recognition across varying views and walking conditions. It takes samples collected from different views of the same subject as a feature set and uses a subspace to represent such information. Then, the similarity of two subjects is measured by the distance between two subspaces and a simple yet effective Weighted Subspace Distance (WSD) algorithm is applied to calculate the similarity. There are two notable advantages of our proposed method: 1) we need not know the exact view of the test gait sequence in advance, and 2) some extent of intra-subject variations can be effectively handled. Experimental results on two benchmark multi-view gait databases are presented to demonstrate the effectiveness of the proposed method.