Estimation of view angles for gait using a robust regression method

Authors:
De Zhang;Yunhong Wang;Zhaoxiang Zhang;Maodi Hu
Affiliations:
State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China 100191 and Laboratory of Intelligent Recognition and Image Processing, School of Computer Science ...;State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China 100191 and Laboratory of Intelligent Recognition and Image Processing, School of Computer Science ...;State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China 100191 and Laboratory of Intelligent Recognition and Image Processing, School of Computer Science ...;State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China 100191 and Laboratory of Intelligent Recognition and Image Processing, School of Computer Science ...
Venue:
Multimedia Tools and Applications
Year:
2013

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Abstract

The performance of most gait recognition methods would drop down if the viewpoint of test data is different from the viewpoint of training data. In this paper, we present an idea of estimating the view angle of a test sample in advance so as to compare it with the corresponding training samples with the same or approximate viewpoint. In order to obtain reliable estimation results, the view-sensitive features should be extracted. We propose a novel and effective feature extraction method to characterize the silhouettes from different views. The discrimination power of this representation is also verified through experiments. Afterwards, the robust regression method is employed to estimate the viewpoint of gait. The view angles of test samples from BUAA-IRIP Gait Database are estimated with the regression models learned from CASIA Gait Database. Compared with the ground truth angles, such estimation is satisfactory with a small error level. Therefore, it can provide necessary help for gait application systems when the view angles of test data are uncertain. This point is verified experimentally through integrating the view angle estimation into a gait based gender classification system.