Human gait recognition based on hybrid-dimensional features from infrared motion images

Gait recognition, also called gait-based human identification, is a relatively new research direction in biometrics. It aims to discriminate individuals by the way they walk. This paper describes a human recognition algorithm by combining three-dimensional and two-dimensional features of the infrared gait. The similarity of the human models and image was measured using the pose evaluation function which included the boundary and region characteristic. A hierarchical search strategy was used to extract the lower body joint angles. And then the peak values of Radon transform from 2D human silhouettes were also attained. Finally, we carry out the human infrared gait recognition based on SVM using the hybrid-dimensional features. Multiple feature fusion is also executed at feature level, and the recognition results demonstrate that the performance of multiple features is better than any single feature.

[1]  Carlos Orrite-Uruñuela,et al.  Human Recognition by Gait Analysis Using Neural Networks , 2002, ICANN.

[2]  Mark S. Nixon,et al.  Automated person recognition by walking and running via model-based approaches , 2004, Pattern Recognit..

[3]  Sudeep Sarkar,et al.  The humanID gait challenge problem: data sets, performance, and analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Aaron F. Bobick,et al.  Gait recognition from time-normalized joint-angle trajectories in the walking plane , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[6]  Sarunas Raudys.  How good are support vector machines? , 2000, Neural Networks.

[7]  Hans-Hellmut Nagel,et al.  Tracking Persons in Monocular Image Sequences , 1999, Comput. Vis. Image Underst..

[8]  Bir Bhanu,et al.  Individual recognition using gait energy image , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[10]  Dong Ming,et al.  Gait recognition based on multiple views fusion of wavelet descriptor and human skeleton model , 2009, 2009 IEEE International Conference on Virtual Environments, Human-Computer Interfaces and Measurements Systems.

[11]  Bir Bhanu,et al.  Individual recognition by kinematic-based gait analysis , 2002, Object recognition supported by user interaction for service robots.

[12]  Larry S. Davis,et al.  EigenGait: Motion-Based Recognition of People Using Image Self-Similarity , 2001, AVBPA.

[13]  M. Nixon,et al.  Automated Human Recognition by Gait using Neural Network , 2008, 2008 First Workshops on Image Processing Theory, Tools and Applications.

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

[15]  Mark S. Nixon,et al.  Gait Extraction and Description by Evidence-Gathering , 1999 .

[16]  Nikolaos V. Boulgouris,et al.  Gait Recognition Using Radon Transform and Linear Discriminant Analysis , 2007, IEEE Transactions on Image Processing.

[17]  Tieniu Tan,et al.  Kinematics-based tracking of human walking in monocular video sequences , 2004, Image Vis. Comput..