Enhancing gait based person identification using joint sparsity model and ℓ1-norm minimization

We consider the problem of person identification using gait sequences under normal, carrying bag and different clothing conditions as the main concern. It has been demonstrated that Gait Energy Image (GEI) can attain a better gait recognition rate under normal conditions. However, it has been shown that GEI is not robust enough to handle the carrying bags and different clothing conditions. Instead of GEI, there are several appearance based gait features in the available literature to reduce the effect of covariate factors by keeping dynamic parts and removing the static parts of the gait features under the assumption that the carrying bags and different clothing conditions affect mostly the static parts. It is however shown in the literature that the static parts also contain valuable information and removal of certain static parts such as head by mistake thigh typed certainly decreases the recognition rate.Our main objective has been to increase the gait recognition rate on different clothing and carrying bag covariate gait sequences. Therefore instead of removing static parts, the Joint Sparsity Model (JSM) is applied to identify the carrying bags and different clothings conditions from GEI features. If a set of GEI feature vectors is submitted to JSM model then a common component and an innovations component for each GEI feature are obtained. The innovations component that has unique characteristic to each of features is considered to identify the covariate conditions. The identified covariate conditions are removed from GEI features and a novel gait feature called GEI JSM is generated. The dimension of GEI JSM is reduced using Random Projection (RP) approach and ? 1 -norm minimization technique based sparse representation is used for classification. It is demonstrated that the RP and ? 1 -norm minimization based sparse representation approach provides statistically significant better results than that of the existing individual identification approaches.

[1]  Stan Z. Li,et al.  Face recognition based on nearest linear combinations , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[2]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Heikki Mannila,et al.  Random projection in dimensionality reduction: applications to image and text data , 2001, KDD '01.

[4]  Shaogang Gong,et al.  Gait recognition without subject cooperation , 2010, Pattern Recognit. Lett..

[5]  J. Condell,et al.  PRWGEI: Poisson random walk based gait recognition , 2011, 2011 7th International Symposium on Image and Signal Processing and Analysis (ISPA).

[6]  Saurabh Jain,et al.  Application of l1-Norm Minimization Technique to Image Retrieval , 2009 .

[7]  Mark S. Nixon,et al.  Exploratory factor analysis of gait recognition , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[8]  Enrico Magli,et al.  Distributed Compressed Sensing , 2015 .

[9]  Baoxin Li,et al.  A compressive sensing approach for expression-invariant face recognition , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Rama Chellappa,et al.  Secure and Robust Iris Recognition Using Random Projections and Sparse Representations , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Mark S. Nixon,et al.  The Effect of Time on Gait Recognition Performance , 2012, IEEE Transactions on Information Forensics and Security.

[12]  A. Murat Tekalp,et al.  Stochastic kinematic modeling and feature extraction for gait analysis , 2003, IEEE Trans. Image Process..

[13]  Michael Elad,et al.  On the stability of the basis pursuit in the presence of noise , 2006, Signal Process..

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

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

[16]  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).

[17]  Junying Gan,et al.  An over-complete sparse representation approach for face recognition under partial occlusion , 2011, Proceedings 2011 International Conference on System Science and Engineering.

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

[19]  E. Candès,et al.  Stable signal recovery from incomplete and inaccurate measurements , 2005, math/0503066.

[20]  Mark S. Nixon,et al.  Covariate Analysis for View-Point Independent Gait Recognition , 2009, ICB.

[21]  J. Cutting,et al.  Gait Perception as an Example of How We May Perceive Events , 1981 .

[22]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[23]  Mark S. Nixon,et al.  Bayesian statistics and modelling , 2020, Nature Reviews Methods Primers.

[24]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[25]  Chen Wang,et al.  Human Identification Using Temporal Information Preserving Gait Template , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[27]  Yanxi Liu,et al.  Shape Variation-Based Frieze Pattern for Robust Gait Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Wei Xiong,et al.  Active energy image plus 2DLPP for gait recognition , 2010, Signal Process..

[29]  Girijesh Prasad,et al.  The Use of Dynamic and Static Characteristics of Gait for Individual Identification , 2009, 2009 13th International Machine Vision and Image Processing Conference.

[30]  David J. Kriegman,et al.  Clustering appearances of objects under varying illumination conditions , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[31]  Yanxi Liu,et al.  Gait Sequence Analysis Using Frieze Patterns , 2002, ECCV.

[32]  Rama Chellappa,et al.  Identification of humans using gait , 2004, IEEE Transactions on Image Processing.

[33]  Xiaoming Huo,et al.  Uncertainty principles and ideal atomic decomposition , 2001, IEEE Trans. Inf. Theory.

[34]  Xuelong Li,et al.  Gait Components and Their Application to Gender Recognition , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[35]  Dong Xu,et al.  Human Gait Recognition Using Patch Distribution Feature and Locality-Constrained Group Sparse Representation , 2012, IEEE Transactions on Image Processing.

[36]  Shaogang Gong,et al.  Gait Representation Using Flow Fields , 2009, BMVC.

[37]  Girijesh Prasad,et al.  Individual Identification Using Gait Sequences under Different Covariate Factors , 2009, ICVS.

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

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

[40]  Riccardo Russo,et al.  A Student's Guide to Analysis of Variance , 1999 .

[41]  Sridha Sridharan,et al.  Compressive Sensing for Gait Recognition , 2011, 2011 International Conference on Digital Image Computing: Techniques and Applications.

[42]  R.G. Baraniuk,et al.  Distributed Compressed Sensing of Jointly Sparse Signals , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[43]  Jie Yang,et al.  Gait recognition based on dynamic region analysis , 2008, Signal Process..

[44]  Shaogang Gong,et al.  Gait recognition using Gait Entropy Image , 2009, ICDP.

[45]  Richard G. Baraniuk.  New Theory and Algorithms for Compressive Sensing , 2009 .

[46]  Pierre Vandergheynst,et al.  Compressed Sensing and Redundant Dictionaries , 2007, IEEE Transactions on Information Theory.

[47]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.