Human gait recognition based on motion analysis including ankle to foot angle measurement

This paper proposes an intelligent authentication of a human using various feature extraction. In this methodology, the silhouettes are pre processed to remove the noises to attain the extraction of various angles, including ankle to foot. From the head to foot almost six angle values are calculated using skeletonization method . Height and width of the silhouettes are also measured. Using the multi class SVM the effective human gait recognition is obtained.

[1]  Joonki Paik,et al.  Gait recognition using active shape model and motion prediction , 2010 .

[2]  Hassan Ghasemzadeh,et al.  A Method for Extracting Temporal Parameters Based on Hidden Markov Models in Body Sensor Networks With Inertial Sensors , 2009, IEEE Transactions on Information Technology in Biomedicine.

[3]  Sudeep Sarkar,et al.  Effect of silhouette quality on hard problems in gait recognition , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[4]  Han Su,et al.  Human gait recognition based on motion analysis , 2005, 2005 International Conference on Machine Learning and Cybernetics.

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

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

[7]  Mark S. Nixon,et al.  Automatic Recognition by Gait , 2006, Proceedings of the IEEE.

[8]  Dimitrios Hatzinakos,et al.  Gait recognition using linear time normalization , 2006, Pattern Recognit..

[9]  Gang Song,et al.  Automatic video-based face verification and recognition by support vector machines , 2003, International Symposium on Multispectral Image Processing and Pattern Recognition.

[10]  E. Kirubakaran,et al.  Image and Formula Based Gait Recognition Methods , 2010 .

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