A new method of gait recognition independent of view angle

The research of gait independent of view angle has become an urgent problem to be solved for gait recognition. In this paper, we propose a gait recognition method that the subject can walk at an arbitrary angle. The gait is detected through background subtraction technique. The contour is represented by a novel approach which includes not only the spatial body contour but also the temporal information. To prove be independent of view angle, the relationship model of the walking azimuth angle and the reference view has been modeled. We transform the walking angle to the most similar canonical views angle. Three canonical views angles are adopted in this paper. The errors which come from transformation when the walk angle is far from the only canonical view will greatly be reduced. We test our method on multi-views database. The correct classification ratios show that our method is a nice try.

[1]  Dimitris N. Metaxas,et al.  Human Gait Recognition , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[2]  James W. Davis,et al.  The Recognition of Human Movement Using Temporal Templates , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Mark S. Nixon,et al.  Markerless Human Gait Analysis via Image Sequences , 2003 .

[4]  Trevor Darrell,et al.  Integrated face and gait recognition from multiple views , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Nanning Zheng,et al.  Silhouette quality quantification for gait sequence analysis and recognition , 2009, Signal Process..

[6]  Han Su,et al.  A gait recognition method using L1-PCA and LDA , 2009, 2009 International Conference on Machine Learning and Cybernetics.

[7]  Rama Chellappa,et al.  Towards a view invariant gait recognition algorithm , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[8]  W. Eric L. Grimson,et al.  Gait analysis for recognition and classification , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[9]  Tieniu Tan,et al.  Fusion of static and dynamic body biometrics for gait recognition , 2003, IEEE Transactions on Circuits and Systems for Video Technology.

[10]  Han Su,et al.  A New Method for Human Gait Recognition Using Temporal Analysis , 2005, CIS.

[11]  Mark S. Nixon,et al.  Advances in automatic gait recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[12]  David Zhang,et al.  Human gait recognition by the fusion of motion and static spatio-temporal templates , 2007, Pattern Recognit..

[13]  Haihong Hu,et al.  Frame difference energy image for gait recognition with incomplete silhouettes , 2009, Pattern Recognit. Lett..

[14]  TianJie,et al.  Frame difference energy image for gait recognition with incomplete silhouettes , 2009 .

[15]  Aaron F. Bobick,et al.  A Multi-view Method for Gait Recognition Using Static Body Parameters , 2001, AVBPA.

[16]  Tieniu Tan,et al.  Modelling the Effect of View Angle Variation on Appearance-Based Gait Recognition , 2006, ACCV.

[17]  Tieniu Tan,et al.  Gait Recognition Based on Fusion of Multi-view Gait Sequences , 2006, ICB.

[18]  Chung-Lin Huang,et al.  Gait Analysis For Human Identification Through Manifold Learning and HMM , 2007, 2007 IEEE Workshop on Motion and Video Computing (WMVC'07).