Gait recognition using temporal gradient patterns

In this paper, a Temporal Gradient Patterns (TGP) method is proposed for gait recognition. The method first computes the gradients of the silhouette in each image. Subsequently, the gradient of each pixel determines the bin number to which the pixel belongs to. The bin number of current and next frame jointly cast a vote to the corresponding index in the matrix of oriented gradients. The obtained matrix henceforth encodes the gradient pattern of each pixel in the gait cycle. The TGP method not only describes the spatial silhouette shapes but also implicitly captures the silhouette deformation in temporal axis. Experimental results show that the proposed approach attains a promising recognition rates.

[1]  Sabesan Sivapalan.  Human identification from video using advanced gait recognition techniques , 2014 .

[2]  Tieniu Tan,et al.  Automatic gait recognition based on statistical shape analysis , 2003, IEEE Trans. Image Process..

[3]  Ralph Gross,et al.  The CMU Motion of Body (MoBo) Database , 2001 .

[4]  Luís Ducla Soares,et al.  Frontal gait recognition combining 2D and 3D data , 2012, MM&Sec '12.

[5]  Rama Chellappa,et al.  A hidden Markov model based framework for recognition of humans from gait sequences , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[6]  Marco Grangetto,et al.  Gait characterization using dynamic skeleton acquisition , 2013, 2013 IEEE 15th International Workshop on Multimedia Signal Processing (MMSP).

[7]  Yuanyuan Zhang,et al.  Real Time Gait Recognition System Based on Kinect Skeleton Feature , 2014, ACCV Workshops.

[8]  Tieniu Tan,et al.  A Framework for Evaluating the Effect of View Angle, Clothing and Carrying Condition on Gait Recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[9]  Mark S. Nixon,et al.  Gait Recognition, Model-Based , 2009, Encyclopedia of Biometrics.

[10]  Gerhard Rigoll,et al.  2.5D gait biometrics using the Depth Gradient Histogram Energy Image , 2012, 2012 IEEE Fifth International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[11]  Max Mignotte,et al.  Analyzing gait pathologies using a depth camera , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[12]  Tae-Seong Kim,et al.  Depth video-based gait recognition for smart home using local directional pattern features and hidden Markov model , 2014 .

[13]  Matti Pietikäinen,et al.  Dynamic Texture Based Gait Recognition , 2009, ICB.

[14]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Chin-Pan Huang,et al.  Human Action Recognition Using Histogram of Oriented Gradient of Motion History Image , 2011, 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control.

[16]  James M. Keller,et al.  Resident identification using kinect depth image data and fuzzy clustering techniques , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[17]  Alexander Belyaev,et al.  GEI + HOG for Action Recognition , 2012 .

[18]  Yasushi Makihara,et al.  The OU-ISIR Gait Database Comprising the Large Population Dataset and Performance Evaluation of Gait Recognition , 2012, IEEE Transactions on Information Forensics and Security.

[19]  Dimitrios Tzovaras,et al.  Gait Recognition Using Compact Feature Extraction Transforms and Depth Information , 2007, IEEE Transactions on Information Forensics and Security.

[20]  Dimitris Kastaniotis,et al.  Gait-based gender recognition using pose information for real time applications , 2013, 2013 18th International Conference on Digital Signal Processing (DSP).

[21]  Sridha Sridharan,et al.  Gait energy volumes and frontal gait recognition using depth images , 2011, 2011 International Joint Conference on Biometrics (IJCB).