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A Study on Security and Surveillance System Using Gait Recognition

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Intelligent Techniques in Signal Processing for Multimedia Security

Part of the book series: Studies in Computational Intelligence ((SCI,volume 660))

Abstract

Security is an important aspect and international attention in smart environments. The surveillance cameras are deployed in all commercial and public places in order to improve the security against terrorism activities. Nowadays, more and more government and industry resources are involved in the researches of security systems, especially in multimedia security, i.e., to enforce security measures, from the images and videos taken from suspicious environment. Therefore, there exists a need to ensure the originality and authenticity of multimedia data as well as to extract intelligent information from enormous images/video streams taken from suspicious environments to build stronger security systems. In this scenario, person identification plays a major role in security systems from the footages of suspicious environments. Without any human assistance, video analyst can identify the person from a large number of videos.

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References

  1. Wang J, She M, Nahavandi S, Kouzani A (2010) A review of vision-based gait recognition methods for human identification. In: International conference on digital image computing: techniques and applications (DICTA), pp 320–327

    Google Scholar 

  2. Yun J (2011) User identification using gait patterns on UbiFloorII. Sensors 11:2611–2639. doi:10.3390/s110302611

    Article  Google Scholar 

  3. Zheng S, Huang K, Tan T, Tao D (2012) A cascade fusion scheme for gait and cumulative foot pressure image recognition. Pattern Recognit 45:3603–3610

    Article  Google Scholar 

  4. Arora P, Srivastava S (2015) Gait recognition using gait Gaussian image. In: 2nd international conference on signal processing and integrated networks (SPIN), pp 791–794

    Google Scholar 

  5. Mohammed S, Saméb A, Oukhellou L, Kong K, Huoa W, Amirat Y (2016) Recognition of gait cycle phases using wearable sensors. Robot Auton Syst 75:50–59

    Article  Google Scholar 

  6. Yogarajah P, Chaurasia P, Condell J, Prasad G (2015) Enhancing gait based person identification using joint sparsity model and ‘1-norm minimization. Inf Sci 308:3–22

    Article  Google Scholar 

  7. Xing X, Wang K, Yan T, Lv Z (2016) Complete canonical correlation analysis with application to multi-view gait recognition. Pattern Recognit 50:107–117

    Article  Google Scholar 

  8. Muramatsu D, Shiraishi A, Makihara Y, Uddin MZ, Yagi Y (2015) Gait-based person recognition using arbitrary view transformation model. IEEE Trans Image Process 24:1

    Article  MathSciNet  Google Scholar 

  9. Choudhury SD, Tjahjadi T (2015) Robust view-invariant multi scale gait recognition. Pattern Recognit 48:798–811

    Article  Google Scholar 

  10. CC Charalambous, AA Bharath (2015) Viewing angle effect on gait recognition using joint kinematics. In: Sixth international conference on imaging for crime prevention and detection (ICDP-15), pp 1–6

    Google Scholar 

  11. Zheng S, Zhang J, Huang K, He R, Tan T (2012) Robust view transformation model for gait recognition 7(2):22–26

    Google Scholar 

  12. Burhan IM, Nordin MJ (2015) Multi-view gait recognition using Enhanced gait energy image and radon transform techniques. Asian J Appl Sci 8(2):138–148

    Article  Google Scholar 

  13. Zhao X, Jiang Y, Stathaki T, Zhang H (2016) Gait recognition method for arbitrary straight walking paths using appearance conversion machine. Neurocomputing 173:530–540

    Article  Google Scholar 

  14. Nandy A, Chakraborty P (2015) A new paradigm of human gait analysis with kinect. In: IEEE eight international conference on contemporary computing, pp 443–448

    Google Scholar 

  15. Prakash C, Mittal A, Kumar R, Mittal N (2015) Identification of spatio-temporal and kinematics parameters for 2-D optical gait analysis system using passive markers. In: IEEE international conference on computer engineering and applications, pp 143–149

    Google Scholar 

  16. Perez-Sala X, Escalera S, Angulo C, Gonzàlez J (2014) A survey on model based approaches for 2D and 3D visual human pose recovery. Sensors 14:4189–4210. doi:10.3390/s140304189

    Article  Google Scholar 

  17. Tafazzoli F, Safabakhsh R (2010) Model-based human gait recognition using leg and arm movements. Eng Appl Artif Intell 23(2010):1237–1246

    Article  Google Scholar 

  18. Lu W, Zong W, Xing W, Bao E (2014) Gait recognition based on joint distribution of motion angles. J Vis Lang Comput 25(6):754–763

    Article  Google Scholar 

  19. Yam CY, Nixon MS, Carter JN (2004) Automated person recognition by walking and running via model-based approaches. Pattern Recognit 37:1057–1072

    Article  Google Scholar 

  20. Ioannidis D, Tzovaras D, Damousis IG, Argyropoulos S, Moustakas K (2007) Gait recognition using compact feature extraction transforms and depth information. IEEE Trans Inf Forensics Secur 2(3):623

    Article  Google Scholar 

  21. Zhang R, Vogler C, Metaxas D (2004) Human gait recognition. In: Proceedings of conference on computer vision and pattern recognition workshop. doi:10.1109/CVPR.2004.87

  22. Cunado D, Nixon MS, Carter JN (2003) Automatic extraction and description of human gait models for recognition purposes. Comput Vis Image Underst 90(1):1–41

    Article  Google Scholar 

  23. Zhang R, Vogler C, Metaxas D (2007) Human gait recognition at sagittal plane. Image Vis Comput 25:321–330

    Article  Google Scholar 

  24. Bouchrika I (2015) Parametric elliptic Fourier descriptors for automated extraction of gait features for people identification. In: 12th international symposium on programming and systems (ISPS), pp 1–7

    Google Scholar 

  25. Vera-Rodrigueza R, Fierreza J, Masonb JSD, Ortega-Garciaa J (2013) A novel approach of gait recognition through fusion with footstep information. In: IEEE international conference on biometrics, pp 1–6

    Google Scholar 

  26. Gafurov D, Snekkenes E (2009) Gait recognition using wearable motion recording sensors. EURASIP J Adv Signal Process Article ID 415817. doi:10.1155/2009/415817

  27. Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors 12:2255–2283. doi:10.3390/s120202255

    Article  Google Scholar 

  28. Ngo TT, Makihara Y, Nagahara H, Mukaigawa Y, Yagi Y (2014) The largest inertial sensor-based gait database and performance evaluation of gait-based personal authentication. Pattern Recognit 47:228–237

    Article  Google Scholar 

  29. Sprager S, Juric MB (2015) Inertial sensor-based gait recognition: a review. Sensors 15:22089–22127. doi:10.3390/s150922089

    Article  Google Scholar 

  30. Wu F, Zhao H, Zhao Y, Zhong H (2015) Development of a wearable-sensor-based fall detection system. Int J Telemed Appl, Article ID 576364. doi:10.1155/2015/576364

  31. Yan Z, Wang Z, Xie H (2008) The application of mutual information-based feature selection and fuzzy LS-SVM-based classifier in motion classification. Comput Methods Programs Biomed 9:275–284

    Article  Google Scholar 

  32. Moustakas K, Tzovaras D, Stavropoulos G (2010) Gait recognition using geometric features and soft biometrics. IEEE Signal Process Lett 17(4):367–370

    Article  Google Scholar 

  33. Lam THW, Lee RST, Zhang D (2007) Human gait recognition by the fusion of motion and static spatio-temporal templates. Pattern Recognit 40:2563–2573

    Article  MATH  Google Scholar 

  34. Tafazzoli F, Bebis G, Louis S, Hussain M (2015) Genetic feature selection for gait recognition. J Electron Imaging 24(1):013036. doi:10.1117/1.JEI.24.1.013036

    Article  Google Scholar 

  35. Johansson G (1973) Visual perception of biological motion and a model for its analysis. Percept Psychophys 14(2):201–211

    Article  Google Scholar 

  36. Lee L, Grimson WEL (2002) Gait analysis for recognition and classification. In: IEEE conference on face and gesture recognition, pp 155–161

    Google Scholar 

  37. Begg R, Kamruzzaman J (2005) A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data. J Biomech 38(3):401–408

    Article  Google Scholar 

  38. Xiao F, Hua P, Jin L, Bin Z (2010) Human gait recognition based on skeletons. In: International conference on educational and information technology (ICEIT 2010), pp 1–5

    Google Scholar 

  39. Luo J, Zhang J, Zi C, Niu Y, Tian H, Xiu C (2015) Gait recognition using GEI and AFDEI. Int J Opt Article ID 763908

    Google Scholar 

  40. Choudhury SD, Tjahjadi T (2012) Silhouette-based gait recognition using procrustes shape analysis and elliptic Fourier descriptors. Pattern Recogn 45:3414–3426

    Article  Google Scholar 

  41. BenAbdelkader C, Cutler R, Davis L (2004) Gait recognition using image self-similarity. EURASIP J Appl Signal Process 4:572–585

    Article  Google Scholar 

  42. Choudhury SD, Tjahjadi T (2013) Gait recognition based on shape and motion analysis of silhouette contours. Comput Vis Image Underst 117(12):1770–1785

    Article  Google Scholar 

  43. Sivapalan S, Chen D, Denman S, Sridharan S, Fookes C (2011) Gait energy volumes and frontal gait recognition using depth images. In: IEEE international joint conference on biometrics, pp 1–6

    Google Scholar 

  44. Kovač J, Peer P (2014) Human skeleton model based dynamic features for walking speed invariant gait recognition. Math Probl Eng Article ID 484320

    Google Scholar 

  45. Lu H, Plataniotis KN, Venetsanopoulos AN (2008) A full-body layered deformable model for automatic model-based gait recognition. EURASIP J Adv Signal Process Article ID 261317. doi:10.1155/2008/261317

  46. Kusakunniran W (2014) Attribute-based learning for gait recognition using spatio-temporal interest points. Image Vis Comput 32(12):1117–1126

    Article  Google Scholar 

  47. Middleton L, Buss AA, Bazin AA, Nixon MS (2005) A floor sensor system for gait recognition. Fourth IEEE workshop on automatic identification advanced technologies, pp 171–176

    Google Scholar 

  48. Tafazzoli F, Bebis G, Louis S, Hussain M (2014) Improving human gait recognition using feature selection. In: Bebis G et al (eds) ISVC 2014, part II, LNCS vol 8888, pp 830–840

    Google Scholar 

  49. Jiwen Lu, Zhang E (2007) Gait recognition for human identification based on ICA and fuzzy SVM through multiple views fusion. Pattern Recognit Lett 28:2401–2411

    Article  Google Scholar 

  50. Narasimhulu GV, Jilani SAK (2012) Fuzzy principal component analysis based gait recognition. Int J Comput Sci Inf Technol 3(3):4015–4020

    Google Scholar 

  51. Das SR, Wilson RC, Lazarewicz MT, Finkel LH (2006) Two-stage PCA extracts spatiotemporal features for gait recognition. J Multimed 1(5):9–17

    Google Scholar 

  52. Luo C, Xu W, Zhu C (2015) Robust gait recognition based on partitioning and canonical correlation analysis. IEEE

    Google Scholar 

  53. D Skoda, P Kutilek, V Socha, J Schlenker, A Ste, J Kalina (2015) The estimation of the joint angles of upper limb during walking using fuzzy logic system and relation maps. In: IEEE 13th international symposium on applied machine intelligence and informatics

    Google Scholar 

  54. Fazli S, Askarifar H, Tavassoli MJ (2011) Gait recognition using SVM and LDA. In: International conference on advances in computing, control, and telecommunication technologies, pp 106–109

    Google Scholar 

  55. Libin DU, Wenxin SHAO (2011) An algorithm of gait recognition based on support vector machine. J Comput Inf Syst 7(13):4710–4715

    Google Scholar 

  56. Taborri J, Rossi S, Palermo E, Patanè F, Cappa P (2014) A novel HMM distributed classifier for the detection of gait phases by means of a wearable inertial sensor network. Sensors 14:16212–16234. doi:10.3390/s140916212

    Article  Google Scholar 

  57. Chen C, Liang J, Zhao H, Hu H, Tian J (2009) Factorial HMM and parallel HMM for gait recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 39(1):114–123

    Google Scholar 

  58. Hai HX, Thuc HLU (2015) Cyclic HMM-based method for pathological gait recognition from side view gait video. Int J Adv Res Comput Eng Technol 4(5):2171–2176

    Google Scholar 

  59. Kale A, Rajagopalan AN, Cuntoor N, Krüger V (2002) Gait-based recognition of humans using continuous HMMs. In: Fifth IEEE international conference on automatic face and gesture recognition, pp 336–341

    Google Scholar 

  60. Zeng W, Wang C (2016) View-invariant gait recognition via deterministic learning. Neurocomputing 175:324–335

    Article  Google Scholar 

  61. Huang S, Elgammal A, Lu J, Yang D (2015) Cross-speed gait recognition using speed-invariant gait templates and globality-locality preserving projections. IEEE Trans Inf Forensics Secur 10(10):2071

    Article  Google Scholar 

  62. Boulgouris NV, Chi ZX (2007) Gait recognition using radon transform and linear discriminant analysis. IEEE Trans Image Process 16(3):731–740

    Article  MathSciNet  Google Scholar 

  63. Okumura M, Iwama H, Makihara Y, Yagi Y (2010) Performance evaluation of vision-based gait recognition using a very large-scale gait database. In: Proceedings of the fourth IEEE international conference on biometrics: theory applications and systems (BTAS), pp 1–6. doi:10.1109/BTAS.2010.5634525

  64. Sarkar S, Jonathon Phillips P, Liu Z, Robledo I, Grother P, Bowyer KW (2005) The human ID gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(2):162–177

    Article  Google Scholar 

  65. Hofmann M, Sural S, Rigoll G (2011) Gait recognition in the presence of occlusion: a new dataset and baseline algorithms. In: 19th international conference on computer graphics, visualization and computer vision, pp 99–104

    Google Scholar 

  66. Gross R, Shi J (2001) The CMU motion of body (MoBo) database. Tech report CMU-RI-TR-01-18, Robotics Institute, Carnegie Mellon University

    Google Scholar 

  67. http://www.gait.ecs.soton.ac.uk/

  68. Ran Y, Zheng Q, Chellappa R, Thomas M (2010) Applications of a simple characterization of human gait in surveillance. IEEE Trans Syst Man Cybern Part B Cybern 40(4):1009–1020

    Google Scholar 

  69. Dey N, Samanta S, Yang XS, Das A, Chaudhuri SS (2013) Optimisation of scaling factors in electrocardiogram signal watermarking using cuckoo search. Int J Bio Inspir Comput 5(5):315–326

    Article  Google Scholar 

  70. Dey N, Mukhopadhyay S, Das A, Chaudhuri SS (2012) Analysis of P-QRS-T components modified by blind watermarking technique within the electrocardiogram signal for authentication in wireless telecardiology using DWT. Int J Image Graph Signal Process 4(7):33

    Article  Google Scholar 

  71. Dey N, Pal M, Das A (2012) A session based watermarking technique within the NROI of retinal fundus images for authentication using DWT, spread spectrum and Harris corner detection. Int J Mod Eng Res 2(3):749–757

    Google Scholar 

  72. Pal AK, Das P, Dey N (2013) Odd–even embedding scheme based modified reversible watermarking technique using Blueprint. arXiv preprint arXiv:1303.5972

  73. Dey N, Dey M, Mahata SK, Das A, Chaudhuri SS (2015) Tamper detection of electrocardiographic signal using watermarked bio–hash code in wireless cardiology. Int J Signal Imaging Syst Eng 8(1–2):46–58

    Article  Google Scholar 

  74. Acharjee S, Chakraborty S, Samanta S, Azar AT, Hassanien AE, Dey N (2014) Highly secured multilayered motion vector watermarking. In: Advanced machine learning technologies and applications. Springer International Publishing, pp 121–134

    Google Scholar 

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Authors and Affiliations

  1. Department of Information Science and Technology, Anna University, Chennai, Chennai, India

    M. Sivarathinabala & S. Abirami

  2. Department of Computer Science and Engineering, Anna University, Chennai, Chennai, India

    R. Baskaran

Authors
  1. M. Sivarathinabala
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  2. S. Abirami
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  3. R. Baskaran
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Correspondence to M. Sivarathinabala .

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Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology Department of Information Technology, Kolkata, India

    Nilanjan Dey

  2. School of Computing Science and Eng., VIT University School of Computing Science and Eng., Vellore, India

    V. Santhi

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Sivarathinabala, M., Abirami, S., Baskaran, R. (2017). A Study on Security and Surveillance System Using Gait Recognition. In: Dey, N., Santhi, V. (eds) Intelligent Techniques in Signal Processing for Multimedia Security. Studies in Computational Intelligence, vol 660. Springer, Cham. https://doi.org/10.1007/978-3-319-44790-2_11

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  • DOI: https://doi.org/10.1007/978-3-319-44790-2_11

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