Skip to main content

A Survey of Behavioral Biometric Gait Recognition: Current Success and Future Perspectives


In today digital society, vulnerability to person authentication is a serious issue in real time scenarios like (airport, hospital, metro stations, etc.). This issue has increased the growth of video surveillance security systems. In recent decades behavioral biometric trait gait has emerged as a potential surveillance monitoring system because of its inconspicuous and unperceivable nature. Even more human gait has a benefit that it can be tracked at a distance and under low resolution videos. Finally, it is difficult to impersonate gait features. In this article, we comprehensively investigate the past and current research development in vision-based (VB) gait recognition. We give a brief description of feature selection and classification techniques used in gait recognition. The article extensively investigates feature representation techniques, classified into model-based and model-free. The article also provides a detail description of databases that are available for research purposes classified into two categories: VB and sensor-based. We extensively examine factors that affect gait recognition, and current research was done to cope with these factors. Moreover, this article proposes future perspectives after investigating state-of-art literature that can be more helpful to experts and new comers in gait recognition. In last, we also give a brief description of the proposed workflow.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16


  1. 1.

    Jain AK, Nandakumar K, Ross A (2016) 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recogn Lett 79:80–105.

    Article  Google Scholar 

  2. 2.

    Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20.

    Article  Google Scholar 

  3. 3.

    Biometric System Market. Accessed: December 20, 2018. [Online].

  4. 4.

    Find Biometrics Global Identity Management [online]. Accessed 10 Feb 2019

  5. 5.

    Lee TKM, Belkhatir M, Sanei S (2014) A comprehensive review of past and present vision-based techniques for gait recognition. Multimed Tools Appl 72(3):2833–2869.

    Article  Google Scholar 

  6. 6.

    Boulgouris NV, Hatzinakos D, Plataniotis KN (2005) Gait recognition: a challenging signal processing technology for biometric identification. IEEE Signal Process Mag 22(6):78–90

    Article  Google Scholar 

  7. 7.

    Kim D, Kim D, Paik J (2010) Gait recognition using active shape model and motion prediction. IET Comput Vision 4(1):25–36.

    Article  Google Scholar 

  8. 8.

    Boyd JE, Little JJ (2005) Biometric gait recognition In: Advanced studies in biometrics, pp 19–42.

  9. 9.

    Masood H, Farooq H (2017) A proposed framework for vision based gait biometric system against spoofing attacks. In: International conference on communication, computing and digital systems (C-CODE), pp. 357–362.

  10. 10.

    Nixon MS, Carter JN (2004) Advances in automatic gait recognition. In: 6th IEEE international conference on automatic face and gesture recognition, pp 139–144.

  11. 11.

    Bashir K, Xiang T, Gong S (2010) Gait recognition without subject cooperation. Pattern Recogn Lett 31(13):2052–2060.

    Article  Google Scholar 

  12. 12.

    Weber W, Weber E (1836) Mechanik der menschlichen Gehwerkzeuge. Dieterich, Göttingen

    Google Scholar 

  13. 13.

    Murray MP, Drought AB, Kory RC (1964) Walking patterns of normal men. J Bone Joint Surg Am 46(2):335–360

    Article  Google Scholar 

  14. 14.

    Murray MP (1967) Gait as a total pattern of movement. Am J Phys Med 46(1):290–333

    Google Scholar 

  15. 15.

    Kale A, Sundaresan A, Rajagopalan AN (2004) Identification of humans using gait. IEEE Trans Image Process 13(9):1163–1173.

    Article  Google Scholar 

  16. 16.

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

    Article  Google Scholar 

  17. 17.

    Wu Z, Huang Y, Wang L, Wang X, Tan T (2016) A comprehensive study on cross-view gait based human identification with deepCNNs. IEEE Trans Pattern Anal Mach Intell 39(2):209–226.

    Article  Google Scholar 

  18. 18.

    Xu W, Luo C, Ji A, Zhu C (2017) Coupled locality preserving projections for cross-view gait recognition. Neurocomputing 224:37–44.

    Article  Google Scholar 

  19. 19.

    Nandy A, Chakraborty R, Chakraborty P (2016) Cloth invariant gait recognition using pooled segmented statistical features. Neuro Comput 191:117–140.

    Article  Google Scholar 

  20. 20.

    Rida I, Jiang X, Marcialis GL (2016) Human body part selection by group lasso of motion for model-free gait recognition. IEEE Signal Process Lett 23(1):154–158.

    Article  Google Scholar 

  21. 21.

    Choudhury SD, Tjahjadi T (2016) Clothing and carrying condition invariant gait recognition based on rotation forest. Pattern Recogn Lett 80:1–7.

    Article  Google Scholar 

  22. 22.

    Mansur A, Makihara Y, Aqmar R, Yagi Y (2014) Gait recognition under speed transition. In: IEEE conference on computer vision and pattern recognition, pp 2521–2528

  23. 23.

    Aqmar MR, Shinoda K, Furui S (2010) Robust gait recognition against speed variation. In: International conference on pattern recognition, pp 2190–2193.

  24. 24.

    Guan Y, Li C-T (2013) A robust speed-invariant gait recognition system for walker and runner identification. In: International conference on biometrics (ICB), pp 1–8.

  25. 25.

    Chen X, Xu J, Weng J (2017) Multi-gait recognition using hypergraph partition. Mach Vis Appl 28(1–2):117–127.

    Article  Google Scholar 

  26. 26.

    Chen X, Weng J, Lu W, Xu J (2017) Multi-gait recognition based on attribute discovery. IEEE Trans Pattern Anal Mach Intell.

    Article  Google Scholar 

  27. 27.

    Chen X, Yang T, Xu J (2016) Multi-gait identification based on multi linear analysis and multi-target tracking. Multimed Tools Appl 75(11):6505–6532.

    Article  Google Scholar 

  28. 28.

    Connor P, Ross A (2018) Biometric recognition by gait: a survey of modalities and features. Comput Vis Image Underst 167:1–27.

    Article  Google Scholar 

  29. 29.

    Sprager S, Juric MB (2015) Inertial sensor-based gait recognition: a review. Sensors 15(9):22089–22127.

    Article  Google Scholar 

  30. 30.

    Tao W, Liu T, Zheng R, Feng H (2012) Gait analysis using wearable sensors. Sensors 12(2):2255–2283.

    Article  Google Scholar 

  31. 31.

    Aggarwal JK, Cai Q (1997) Human motion analysis: a review. In: Proceeding IEEE conference on non rigid and articulated motion workshop, pp 90–102

  32. 32.

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

  33. 33.

    Zhang Z, Hu M, Wang Y (2011) A survey of advances in biometric gait recognition. In: Chinese conference on biometric recognition, pp 150–158

  34. 34.

    Lv Z, Xing X, Wang K, Guan D (2015) Class energy image analysis for video sensor-based gait recognition: a review. Sensors 15(1):932–964.

    Article  Google Scholar 

  35. 35.

    Shirke S, Pawar SS, Shah K (2014) Literature review: model free human gait recognition. In: IEEE computer society fourth international conference on communication systems and network technologies, pp 891–895.

  36. 36.

    Wayman JL (2001) Fundamentals of biometric authentication technologies. Int J Image Graph 01(01):93–113

    Article  Google Scholar 

  37. 37.

    Introduction to Biometric Summer 2006 Lectures [online]. Accessed 17 Oct 2018

  38. 38.

    Lu J, Wang G, Moulin P (2014) Human identity and gender recognition from gait sequences with arbitrary walking directions. IEEE Trans Inf Forensics Secur 9(1):51–61.

    Article  Google Scholar 

  39. 39.

    Yu S, Tan T, Huang K, Jia K, Wu X (2009) A study on gait-based gender classification. IEEE Trans Image Process 18(8):1905–1910.

    MathSciNet  MATH  Article  Google Scholar 

  40. 40.

    De Z (2015) Research on gait based gender classification via fusion of multiple views. Int J Database Theory Appl 8(5):39–50

    Article  Google Scholar 

  41. 41.

    Lu J, Tan Y-P (2010) Gait-based human age estimation. IEEE Trans Inf Forensics Secur 5(4):761–770.

    Article  Google Scholar 

  42. 42.

    Zhang D, Wang Y, Bhanu B (2010) Age classification based on gait using HMM. In: International conference on pattern recognition, pp 3834–3837

  43. 43.

    Weiss RJ, Wretenberg P, Stark A, Palmblad K, Larsson P, Grondal L, Brostrom E (2008) Gait pattern in rheumatoid arthritis. Gait Posture 28(2):229–234

    Article  Google Scholar 

  44. 44.

    Saad A, Zaarour I, Guerin F, Bejjani P, Ayache M, Lefebvre D (2017) Detection of freezing of gait for Parkinson’s disease patients with multi-sensor device and Gaussian neural networks. Int J Mach Learn Cybern 8(3):941–954

    Article  Google Scholar 

  45. 45.

    Tupa O, Prochazka A, Vysata O, Schatz M, Mareš J, Valis M, Marik V (2015) Motion tracking and gait feature estimation for recognising Parkinson’s disease using MS Kinect. Biomed Eng Online 14(1):97

    Article  Google Scholar 

  46. 46.

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

    Article  Google Scholar 

  47. 47.

    Cutting JE, Kozlowski LT (1977) Recognizing friends by their walk: gait perception without familiarity cues. Bull Psychon Soc 9(5):353–356

    Article  Google Scholar 

  48. 48.

    Niyogi S, Adelson E (1994) Analyzing and recognizing walking figures in XYT. In: IEEE computer society conference on computer vision and pattern recognition, pp 469–474

  49. 49.

    Wang L, Tan T, Weiming H, Ning H (2003) Automatic gait recognition based on statistical shape analysis. IEEE Trans Image Process 12(9):1120–1131.

    MathSciNet  Article  Google Scholar 

  50. 50.

    Wang L, Ning H, Tan T, Weiming H (2004) Fusion of static and dynamic body biometrics for gait recognition. IEEE Trans Circuits Syst Video Technol 14(2):149–158.

    Article  Google Scholar 

  51. 51.

    Okuno R, Fujimoto S, Akazawa J, Yokoe M, Sakoda S, Akazawa K (2008) Analysis of spatial temporal plantar pressure pattern during gait in Parkinson’s disease. In: 30th annual international IEEE EMBS conference, pp 1765–1768

  52. 52.

    Zheng S, Huang K, Tan T (2011) Evaluation framework on translation-invariant representation for cumulative foot pressure image. In: 18th IEEE international conference on image processing, pp 201–204

  53. 53.

    Kotti M, Duffell LD, Faisal AA, McGregor AH (2017) Detecting knee osteoarthritis and its discriminating parameters using random forests. Med Eng Phys 43:19–29.

    Article  Google Scholar 

  54. 54.

    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 Recogn 47(1):228–237.

    Article  Google Scholar 

  55. 55.

    Ngo TT, Makihara Y, Nagahara H, Mukaigawa Y, Yagi Y (2015) Similar gait action recognition using an inertial sensor. Pattern Recogn 48(4):1289–1301.

    Article  Google Scholar 

  56. 56.

    Tereso A, Martins MM, Santos CP (2015) Evaluation of gait performance of knee osteoarthritis patients after total knee arthroplasty with different assistive devices. Res Biomed Eng 31(3):208–217.

    Article  Google Scholar 

  57. 57.

    Bergmann JHM et al (2013) An attachable clothing sensor system for measuring knee joint angles. IEEE Sens J 13(10):4090–4097.

    Article  Google Scholar 

  58. 58.

    Bachlin M, Plotnik M, Roggen D, Maidan I, Hausdorff JM, Giladi N, Troster G (2010) Wearable assistant for Parkinson’s disease patients with the freezing of gait symptom. IEEE Trans Inf Technol Biomed 14(2):436–446.

    Article  Google Scholar 

  59. 59.

    Muaaz M, Mayrhofer R (2017) Smartphone-based gait recognition: from authentication to imitation. IEEE Trans Mob Comput 16(11):3209–3221

    Article  Google Scholar 

  60. 60.

    Yoneyama M, Kurihara Y, Watanabe K, Mitoma H (2014) Accelerometry-based gait analysis and its application to Parkinson’s disease assessment—part 1: detection of stride event. IEEE Trans Neural Syst Rehabil Eng 22(3):613–622.

    Article  Google Scholar 

  61. 61.

    Cui X, Zhao Z, Ma C, Chen F, Liao H (2018) A gait character analyzing system for osteoarthritis pre-diagnosis using RGB-D camera and supervised classifier. In: World congress on medical physics and biomedical engineering, IFMBE proceedings, pp 297–301.

  62. 62.

    Mahyuddin A, Mihradi S, Dirgantara T, Moeliono M, Prabowo T (2012) Development of Indonesian gait database using 2D optical motion analyzer system. ASEAN Eng J Part A 2(2):62–72

    Google Scholar 

  63. 63.

    Yun Y, Kim H-C, Shin SY, Lee J, Deshpande AD, Kim C (2014) Statistical method for prediction of gait kinematics with Gaussian process regression. J Biomech 47(1):186–192.

    Article  Google Scholar 

  64. 64.

    Moore JK, Hnat SK, van den Bogert AJ (2015) An elaborate data set on human gait and the effect of mechanical perturbations. PeerJ 3:e918.

    Article  Google Scholar 

  65. 65.

    Ishikawa Y et al (2017) Gait analysis of patients with knee osteoarthritis by using elevation angle: confirmation of the planar law and analysis of angular difference in the approximate plane. Adv Robot 31(1–2):68–79.

    Article  Google Scholar 

  66. 66.

    Iwama H, Okumura M, Makihara Y, Yagi Y (2012) The OU-ISIR gait database comprising the large population dataset and performance evaluation of gait recognition. IEEE Trans Inf Forensics Secur 7(5):1511–1521.

    Article  Google Scholar 

  67. 67.

    Iwashita Y, Ogawara K, Kurazume R (2014) Identification of people walking along curved trajectories. Pattern Recogn Lett 48:60–69.

    Article  Google Scholar 

  68. 68.

    Kastaniotis D, Theodorakopoulos I, Economou G, Fotopoulos S (2013) Gait-based gender recognition using pose information for real time applications. In: 18th international conference on digital signal processing (DSP), pp 1–6

  69. 69.

    Hofmann M, Geiger J, Bachmann S, Schuller B, Rigoll G (2014) The TUM gait from audio, image and depth (GAID) database: multimodal recognition of subjects and traits. Vis Commun Image Represent 25(1):195–206.

    Article  Google Scholar 

  70. 70.

    Borras R, Lapedriza A, Igual L (2012) Depth information in human gait analysis: an experimental study on gender recognition. In: International conference image analysis and recognition (ICIAR), pp 98–105

  71. 71.

    Wang Y, Sun J, Li J, Zhao D (2016) Gait recognition based on 3D skeleton joints captured by kinect. In: IEEE international conference on image processing (ICIP), pp 3151–3155

  72. 72.

    Tang S, Andriluka M, Schiele B (2014) Detection and tracking of occluded people. Int J Comput Vis 110(1):58–69.

    Article  Google Scholar 

  73. 73.

    Bhowmick S, Nandy A, Chakraborty P, Nandi GC (2014) A speed invariant human identification system using gait biometrics. Int J Comput Vis Robot 4(1/2):3–22.

    Article  Google Scholar 

  74. 74.

    Han J, Bhanu B (2006) Individual recognition using gait energy image. IEEE Trans Pattern Anal Mach Intell 28(2):316–322.

    Article  Google Scholar 

  75. 75.

    Kusakunniran W (2014) Recognizing gaits on spatio-temporal feature domain. IEEE Trans Inf Forensics Secur 9(9):1416–1423.

    Article  Google Scholar 

  76. 76.

    Choudhury SD, Tjahjadi T (2012) Silhouette-based gait recognition using Procrustes shape analysis and elliptic Fourier descriptors. Pattern Recogn 45(9):3414–3426.

    Article  Google Scholar 

  77. 77.

    Sarkar S, Phillips PJ, Liu Z, Vega IR, Grother P, Bowyer KW (2005) The HumanID gait challenge problem: data sets, performance, and analysis. IEEE Trans Pattern Anal Mach Intell 27(2):162–177.

    Article  Google Scholar 

  78. 78.

    Sudha LR, Bhavani R (2013) An efficient spatio-temporal gait representation for gender classification. Appl Artif Intell 27(1):62–75.

    Article  Google Scholar 

  79. 79.

    Shao H, Wang Y, Wang Y, Hu W (2016) A preprocessing method for gait recognition. In: International Conference of young computer scientists, engineers and educators, pp 77–86

  80. 80.

    Wang L, Tan T, Ning H, Weiming H (2003) Silhouette analysis-based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25(12):1505–1518.

    Article  Google Scholar 

  81. 81.

    BenAbdelkader C, Cutler R, Davis L (2002) Stride and cadence as a biometric in automatic person identification and verification. In: Proceedings of Fifth IEEE international conference on automatic face gesture recognition, pp 372–377

  82. 82.

    Kusakunniran W, Qiang W, Zhang J, Li H (2012) Gait recognition under various viewing angles based on correlated motion regression. IEEE Trans Circuits Syst Video Technol 22(6):966–980.

    Article  Google Scholar 

  83. 83.

    Li C, Min X, Sun S, Lin W, Tang Z (2017) DeepGait: a learning deep convolutional representation for view-invariant gait recognition using joint Bayesian. Appl Sci.

    Article  Google Scholar 

  84. 84.

    Yeoh T, Zapotecas-Martínez S, Akimoto Y, Aguirre H, Tanaka K (2014) Genetic algorithm assisted by a SVM for feature selection in gait classification. In: International symposium on intelligent signal processing and communication systems (ISPACS), pp 191–195.

  85. 85.

    Tafazzoli F, Bebis G, Louis S, Hussain M (2015) Genetic features election for gait recognition. J Electron Imaging 24(1):013036.

    Article  Google Scholar 

  86. 86.

    Huang S, Elgammal A, Jiwen L, Yang D (2015) Cross-speed gait recognition using speed-invariant gait templates and globality-locality preserving projections. IEEE Trans Inf Forensics Secur 10(10):2071–2083.

    Article  Google Scholar 

  87. 87.

    Iwashita Y, Kakeshita M, Sakano H, Kurazume R (2017) Making gait recognition robust to speed changes using mutual subspace method. IEEE international conference on robotics and automation (ICRA), pp 2273–2278.

  88. 88.

    BenAbdelkader C, Cutler R, Davis L (2002) Motion-based recognition of people in EigenGait space. In: Proceedings of Fifth IEEE international conference on automatic face gesture recognition, pp 267–272.

  89. 89.

    Han J, Bhanu B, Roy-Chowdhury AK (2005)“A study on view-insensitive gait recognition. In: IEEE international conference on image processing, vol 5, pp III–297.

  90. 90.

    Cheng Q, Fu B, Chen H (2009) Gait recognition based on PCA and LDA. In: International symposium on computer science and computational technology (ISCSCI). Academy Publisher, pp 124–127

  91. 91.

    Hongye X, Zhuoya H (2015) Gait recognition based on gait energy image and linear discriminant analysis. In: IEEE international conference on signal processing, communications and computing (ICSPCC), pp 1–4.

  92. 92.

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

    MathSciNet  Article  Google Scholar 

  93. 93.

    Isaac ERHP, Elias S, Rajagopalan S, Easwarakumar KS (2017) View-invariant gait recognition through genetic template segmentation. IEEE Signal Process Lett 24(8):1188–1192.

    Article  Google Scholar 

  94. 94.

    Lishani AO, Boubchir L, Khalifa E, Bouridane A (2017) Human gait recognition based on Haralick features. Signal Image Video Process 11(6):1123–1130.

    Article  Google Scholar 

  95. 95.

    Wang X, Wang J, Yan K (2018) Gait recognition based on Gabor wavelets and (2D)2 PCA. Multimed Tools Appl 77:12545–12561.

    Article  Google Scholar 

  96. 96.

    Tan D, Huang K, Yu S, Tan T (2006) “Efficient Night gait recognition based on template matching. In: 18th international conference on pattern recognition (ICPR), pp 1000–1003

  97. 97.

    Wang L, Hu W, Tan T (2002) A new attempt to gait-based human identification. In: Proceeding of 16th international conference on pattern recognition, pp 115–118

  98. 98.

    Sundaresan A, Chowdhuiy AR, Chellappa R (2003) A hidden markov model based frameworkfor recognition of humans from gait sequences. In: Proceedings of international conference on image processing, pp 93–96

  99. 99.

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

    Article  Google Scholar 

  100. 100.

    Wang C, Zhang J, Wang L, Pu J (2012) X, Human identification using temporal information preserving gait template. IEEE Trans Pattern Anal Mach Intell 34(11):2164–2176.

    Article  Google Scholar 

  101. 101.

    Zheng S, Zhang J, Huang K, He R, Tan T (2011) Robust view transformation model for gait recognition. In: 18th IEEE international conference on image processing, pp 2073–2076.

  102. 102.

    Zhang Z, Troje NF (2005) View-independent person identification from human gait. Neurocomputing 69:250–256.

    Article  Google Scholar 

  103. 103.

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

  104. 104.

    KovaI J, Peer P (2014) Human skeleton model based dynamic features for walking speed invariant gait recognition. Math Probl Eng 2014:15.

    Article  Google Scholar 

  105. 105.

    Li W, Kuo C-CJ, Peng J (2018) Gait recognition via GEI subspace projections and collaborative representation classification. Neurocomputing 275:1932–1945.

    Article  Google Scholar 

  106. 106.

    Portillo J et al (2017) Cross view gait recognition using joint-direct linear discriminant analysis. Sensors 17(1):6.

    Article  Google Scholar 

  107. 107.

    Kusakunniran W, Wu Q, Li H, Zhang J (2009) Multiple views gait recognition using view transformation model based on optimized gait energy image. In: 12th international conference on computer vision workshops, pp 1058–1064.

  108. 108.

    Makihara Y, Mansur A, Muramatsu D, Uddin Z, Yagi Y (2015) Multi-view discriminant analysis with tensor representation and its application to cross-view gait recognition. In: 11th IEEE international conference and workshops on automatic face and gesture recognition (FG), pp 1–8.

  109. 109.

    Mansur A, Makihara Y, Muramatsu D, Yagi Y (2014) Cross-view gait recognition using view-dependent discriminative analysis. In: IEEE international joint conference on biometrics, pp 1–8.

  110. 110.

    Ortells J, Mollineda RA, Mederos B, Martín-Felez R (2017) Gait recognition from corrupted silhouettes: a robust statistical approach. Mach Vis Appl 28(1–2):15–33.

    Article  Google Scholar 

  111. 111.

    Castro FM, Marın-Jimenez MJ, Guil N, Perez de la Blanca N (2018) Multimodal feature fusion for CNN-based gait recognition: an empirical comparison. arXiv:abs/1806.07753

  112. 112.

    Li X, Maybank SJ, Yan S, Tao D, Dong X (2008) Gait components and their application to gender recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 38(2):145–155.

    Article  Google Scholar 

  113. 113.

    Nandy A, Pathak A, Chakraborty P (2017) A study on gait entropy image analysis for clothing invariant human identification. Multimed Tools Appl 76(7):9133–9167.

    Article  Google Scholar 

  114. 114.

    Kwolek B, Krzeszowski T, Michalczuk A, Josinski HK (2014) 3D gait recognition using spatio-temporal motion descriptors. In: Asian conference on intelligent information and database systems (ACIIDS), pp 595–604

  115. 115.

    Havasi L, Szlavik Z, Sziranyi T (2006) Higher order symmetry for non-linear classification of human walk detection. Pattern Recogn Lett 27:822–829

    Article  Google Scholar 

  116. 116.

    Kumar R, Phoha VV, Jain A (2015) Treadmill attack on gait-based authentication systems. In: 7th international conference on biometrics theory, applications and systems (BTAS), pp 1–7.

  117. 117.

    Makihara Y, Suzuki A, Muramatsu D, Li X, Yagi Y (2011) Joint intensity and spatial metric learning for robust gait recognition. In: International conference on computer vision, pp 571–578

  118. 118.

    López-Fernández D, Madrid-Cuevas FJ, Carmona-Poyato A, Muñoz-Salinas R, Medina-Carnicer R (2016) A new approach for multi-view gait recognition on unconstrained paths. J Vis Commun Image Represent 38:396–406

    Article  Google Scholar 

  119. 119.

    Kastaniotis D, Theodorakopoulos I, Theoharatos C, Economou G, Fotopoulos S (2015) A framework for gait-based recognition using kinect. Pattern Recogn Lett 68(2):327–335.

    Article  Google Scholar 

  120. 120.

    Connie T, Goh KOM, Teoh ABJ (2016) Multi-view gait recognition using a doubly-kernel approach on the Grassmann manifold. Neurocomputing 216:534–542.

    Article  Google Scholar 

  121. 121.

    Muramatsu D, Makihara Y, Yagi Y (2016) View transformation model incorporating quality measures for cross-view gait recognition. IEEE Trans Cybern.

    Article  Google Scholar 

  122. 122.

    Muramatsu D, Makihara Y, Yagi Y (2015) Cross-view gait recognition by fusion of multiple transformation consistency measures. IET Biom 4(2):62–73.

    Article  Google Scholar 

  123. 123.

    Fernández D et al (2015) Entropy volumes for viewpoint-independent gait recognition. Machine Vision and Applications 26:1079–1094.

    Article  Google Scholar 

  124. 124.

    Haifeng H (2014) Multiview gait recognition based on patch distribution features and uncorrelated multi linear sparse local discriminant canonical correlation analysis. IEEE Trans Circuits Syst Video Technol 24(4):617–630.

    Article  Google Scholar 

  125. 125.

    Yeoh TW, Daolio F, Aguirre HE, Tanaka K (2017) On the effectiveness of feature selection methods for gait classification under different covariate factors. Appl Soft Comput 61:42–57.

    Article  Google Scholar 

  126. 126.

    Begg RK, Palaniswami M, Owen B (2005) Support vector machines for automated gait classification. IEEE Trans Biomed Eng 52(5):828–838

    Article  Google Scholar 

  127. 127.

    Krzeszowski T, Michalczuk A, Kwolek B, Switonski A, Josinski H (2013) Gait recognition based on marker-less 3D motion capture. In: 10th IEEE international conference on advanced video and signal based surveillance, pp 232–237

  128. 128.

    Kusakunniran W, Wu Q, Zhang J, Li H (2012) Cross-view and multi-view gait recognitions based on view transformation model using multi-layer perceptron. Pattern Recogn Lett 33:882–889.

    Article  Google Scholar 

  129. 129.

    Zeng W, Wang C, Li Y (2014) Model-based human gait recognition via deterministic learning. Cognitive Computation 6(2):218–229.

    Article  Google Scholar 

  130. 130.

    Zeng W, Wang C (2015) Gait recognition across different walking speeds via deterministic learning. Neurocomputing 152:139–150.

    Article  Google Scholar 

  131. 131.

    Yoo J-H, Hwang D, Moon K-Y, Nixon MS (2008) Automated human recognition by gait using neural network. In: First workshops on image processing theory, tools and applications (IPTA), pp 1–6

  132. 132.

    Kusakunniran W, Qiang W, Zhang J, Ma Y, Li H (2013) A new view-invariant feature for cross-view gait recognition. IEEE Trans Inf Forensics Secur 8(10):1642–1653.

    Article  Google Scholar 

  133. 133.

    Guan Y, Li C-T, Roli F (2015) On reducing the effect of covariate factors in gait recognition: a classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 37(7):521–1528.

    Article  Google Scholar 

  134. 134.

    Zeng W, Wang C (2012) Human gait recognition via deterministic learning. Neural Netw 35:92–102.

    Article  Google Scholar 

  135. 135.

    Batchuluun G, Yoon HS, Kang JK, Park KR (2018) Gait-based human identification by combining shallow convolutional neural network-stacked long short-term memory and deep convolutional neural network. IEEE Access 6:63164–63186.

    Article  Google Scholar 

  136. 136.

    Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) On input/output architectures for convolutional neural network-based cross-view gait recognition. IEEE Trans Circuits Syst Video Technol 28(1):1.

    Article  Google Scholar 

  137. 137.

    Liu W, Zhang C, Ma H, Li S (2018) Learning efficient spatial-temporal gait features with deep learning for human identification. Neuroinformatics 16:457–471.

    Article  Google Scholar 

  138. 138.

    Alotaibi M, Mahmood A (2015) Improved Gait recognition based on specialized deep convolutional neural networks. In: IEEE applied imagery pattern recognition workshop (AIPR), Washington, DC, pp 1–7.

  139. 139.

    Alotaibi M, Mahmood A (2017) Improved gait recognition based on specialized deep convolutional neural network. Comput Vis Image Underst 164:103–110.

    Article  Google Scholar 

  140. 140.

    Guntor et al (2018) Convolutional neural network (CNN) based gait recognition system using microsoft kinect skeleton features. Int J Eng Technol 7:202-205.

  141. 141.

    Wolf T, Babaee M, Rigoll G (2016) Multi-view gait recognition using 3D convolutional neural networks. In: IEEE international conference on image processing (ICIP), pp 4165–4169.

  142. 142.

    Marın-Jiménez MJ, Castro FM, Guil N, de la Torre F, Medina-Carnicer R (2017) Deep multi-task learning for gait-based biometrics. In: IEEE international conference on image processing (ICIP), pp 106–110

  143. 143.

    Yao L, Kusakunniran W, Wu Q, Zhang J, Tang Z (2018) Robust CNN-based gait verification and identification using skeleton gait energy image. In: Digital image computing: techniques and applications (DICTA), pp 1–7.

  144. 144.

    Battistone F, Petrosino A (2018) TGLSTM: A time based graph deep learning approach to gait recognition. Pattern Recogn Lett.

    Article  Google Scholar 

  145. 145.

    Batchuluun G, Naqvi RA, Kim W, Park KR (2018) Body-movement-based human identification using convolutional neural network. Expert Syst Appl 101:56–77.

    Article  Google Scholar 

  146. 146.

    Tong S, Fu Y, Yue X, Ling H (2018) Multi-view gait recognition based on a spatial-temporal deep neural network. IEEE Access 6:57583–57596.

    Article  Google Scholar 

  147. 147.

    Bouchrika I, Boukrouche A (2014) Markerless extraction of gait features using haar-like template for view-invariant biometrics. In Proceedings of 15th IEEE international conference on sciences and techniques of automatic control and computer engineering (STA), pp 519–524

  148. 148.

    Su Y, Feng Z, Xing M (2018) Spatio-temporal large margin nearest neighbor (St-Lmnn) based on riemannian features for individual identification. In: IEEE international conference on multimedia and expo (ICME), pp 1–6.

  149. 149.

    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

  150. 150.

    Ben Abdelkader C, Cutler R, Davis L (2002) Person identification using automatic height and stride estimation. In: Proceedings of 16th international conference on pattern recognition, vol 4, pp 377–380

  151. 151.

    Yam CY, Nixon MS, Carter JN (2004) Automated person recognition by walking and running via model-based approaches. Pattern Recogn 37(5):1057–1072.

    Article  Google Scholar 

  152. 152.

    Tafazzoli F, Safabakhsh R (2010) Model-based human gait recognition using leg and arm movements. Eng Appl Artif Intell 23(8):1237–1246

    Article  Google Scholar 

  153. 153.

    Yoo J-H, Nixon MS (2011) Automated markerless analysis of human gait motion for recognition and classification. ETRI J 33(2):259–266

    Article  Google Scholar 

  154. 154.

    Zhao G, Liu G, Li H, Pietikainen M (2006) 3D gait recognition using multiple cameras. In: 7th international conference on automatic face and gesture recognition (FGR06), pp 29–534

  155. 155.

    Collins RT, Gross R, Shi J (2002) Silhouette-based human identification from body shape and gait. In: Proceedings of the fifth IEEE international conference on automatic face and gesture recognition, pp 366–371

  156. 156.

    Kusakunniran W, Wu Q, Zhang J, Li H (2011) Speed-invariant gait recognition based on procrustes shape analysis using higher-order shape configuration. In: 18th IEEE international conference on image processing, pp 545–548.

  157. 157.

    Goffredo M, Bouchrika I, Carter JN, Nixon MS (2010) Self-calibrating view-invariant gait biometrics. IEEE Trans Syst Man Cybern Part B Cybern 40(4):997–1008.

    Article  Google Scholar 

  158. 158.

    Kovac J, Struc V, Peer P (2017) Frame–based classification for cross-speed gait recognition. Multimed Tools Appl 99:1–23.

    Article  Google Scholar 

  159. 159.

    Tanawongsuwan R, Bobick A (2001) Gait recognition from time-normalized joint-angle trajectories in the walking plane. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (CVPR), pp 726–731

  160. 160.

    Arora P, Hanmandlub M, Srivastava S (2015) Gait based authentication using gait information image features. Pattern Recogn Lett 68(2):336–342.

    Article  Google Scholar 

  161. 161.

    Verlekar TT, Correia PL, Soares LD (2017) View-invariant gait recognition system using a gait energy image decomposition method. IET Biometrics 6(4):299–306.

    Article  Google Scholar 

  162. 162.

    Mahfouf Z, Merouani HF, Bouchrika I, Harrati N (2018) Investigating the use of motion-based features from optical flow for gait recognition. Neurocomputing 283:140–149.

    Article  Google Scholar 

  163. 163.

    Jia S, Wang L, Li X (2015) View-invariant gait authentication based on silhouette contours analysis and view estimation. IEEE/CAA J Autom Sin 2(2):226–232.

    MathSciNet  Article  Google Scholar 

  164. 164.

    Boulgouris NV, Plataniotis KN, Hatzinakos D (2004) Gait recognition using dynamic time warping. In: IEEE 6th workshop on multimedia signal processing

  165. 165.

    Murase H, Sakai R (1996) Moving object recognition in eigenspace representation: gait analysis and lip reading. Pattern Recogn Lett 17:155–162

    Article  Google Scholar 

  166. 166.

    Świtoński A, Michalczuk A, Josiński H, Polański A, Wojciechowski K (2012) Dynamic time warping in gait classification of motion capture data. Int J Comput Inf Eng 6(11):1289–1294

    Google Scholar 

  167. 167.

    Park J, Lee Y, Ko H (2009) Dynamic time warping based identification using gabor feature of adaptive motion model for walking humans. Int J Control Autom Syst 7(5):817–823.

    Article  Google Scholar 

  168. 168.

    Thyagharajan KK, Kiruba Raji I (2018) A review of visual descriptors and classification techniques used in leaf species identification. Arch Comput Methods Eng.

    Article  Google Scholar 

  169. 169.

    Simonyan K, Zisserman A (2014) Two-stream convolutional networks for action recognition in videos. arXiv:1406.2199

  170. 170.

    Taigman Y, Yang M, Ranzato M, Wolf (2014) DeepFace: closing the gap to human-level performance in face verification. In: CVPR, 2014

  171. 171.

    Takemura N, Makihara Y, Muramatsu D, Echigo T, Yagi Y (2018) “Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition. IPSJ Trans. Comput Vis Appl 10(4):1–14

    Google Scholar 

  172. 172.

    Uddin MZ, Ngo TT, Makihara Y, Takemura N, Li X, Muramatsu D, Yagi Y (2018) The OU-ISIR large population gait database with real-life carried object and its performance evaluation. IPSJ Trans. Comput Visi Appl 10(1):5

    Article  Google Scholar 

  173. 173.

    Xu C, Makihara Y, Ogi G, Li X, Yagi Y, Lu J (2017) The OU-ISIR gait database comprising the large population dataset with age and performance evaluation of age estimation. IPSJ Trans Comput Vis Appl 9(24):1–14

    Google Scholar 

  174. 174.

    Iwashita Y, Kurazume R, Stoica A (2014) Gait identification using invisible shadows: robustness to appearance changes. In: Fifth international conference on emerging security technologies, pp 34–39

  175. 175.

    Lopez-Fernandez D, Madrid-Cuevas FJ, Carmona-Poyato A, Marın-Jimnez MJ, Munoz Salinas R (2014) The AVA multi-view dataset for gait recognition. In: International workshop on activity monitoring by multiple distributed sensing, pp 26–39

  176. 176.

    Makihara Y, Mannami H, Tsuji A, Hossain MA, Sugiura K, Mori A, Yagi Y (2012) The OU-ISIR gait database comprising the treadmill dataset. IPSJ Trans Comput Vis Appl 4:53–62

    Article  Google Scholar 

  177. 177.

    Yin Y, Liu L, Sun X (2011) “SDUMLA-HMT: a multimodal biometric database. In: The 6th Chinese conference on biometric recognition (CCBR), LNCS 7098, Beijing, China, pp 260–268

  178. 178.

    Iwashita Y, Baba R, Ogawara K, Kurazume R (2010) Person identification from spatio-temporal 3D gait. In: International conference on emerging security technologies, pp 30–35

  179. 179.

    Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: 18th international conference on pattern recognition (ICPR), pp 441–444

  180. 180.

    Gross R, Shi J (2001) The CMU motion of body (MoBo) database, Technical Report CMU-RI-TR-01-18, Robotics Institute, Pittsburgh

  181. 181.

    Johnson AY, Bobick AF (2001) A multi-view method for gait recognition using static body parameters. In: International conference on audio- and video-based biometric person authentication (AVBPA), pp 301–311

  182. 182.

    Little JJ, Boyd JE (1998) Recognizing people by their gait: the shape of motion. Videre J Comput Vis Res 1(2):1–32

    Google Scholar 

  183. 183.

    Choi S, Kim J, Kim W, Kim C (2019) Skeleton-based gait recognition via robust frame-level matching. IEEE Trans Inf Forensics Secur.

    Article  Google Scholar 

  184. 184.

    Khandelwal S, Wickstrom N (2017) Evaluation of the performance of accelerometer-based gait event detection algorithms in different real-world scenarios using the MAREA gait database. Gait Posture 51:84–90

    Article  Google Scholar 

  185. 185.

    Zhang Y, Pan G, Jia K, Lu M, Wang Y, Wu Z (2015) Accelerometer-based gait recognition by sparse representation of signature points with clusters. IEEE Trans Cybern 45(9):1864–1875.

    Article  Google Scholar 

  186. 186.

    Chattopadhyay P, Sural S, Mukherjee J (2015) Frontal gait recognition from occluded scenes. Pattern Recogn Lett 63:9–15.

    Article  Google Scholar 

  187. 187.

    Deng M, Wang C (2018) Human gait recognition based on deterministic learning and data stream of Microsoft Kinect. IEEE Trans Circuits Syst Video Technol.

    Article  Google Scholar 

  188. 188.

    Bobick AF, Johnson AY (2001) Gait recognition using static, activity-specific parameters. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (CVPR), vol 1, pp 423–430

  189. 189.

    Wagg DK, Nixon MS (2004) On automated model-based extraction and analysis of gait. In: Proceedings of Sixth IEEE international conference on automatic face and gesture recognition, pp 11–16

  190. 190.

    Bouchrika I, Nixon MS (2007) Model-based feature extraction for gait analysis and recognition. In: International conference on computer vision/computer graphics collaboration techniques and applications, pp 150–160

  191. 191.

    Urtasun R, Fua P (2004) 3D tracking for gait characterization and recognition. In: Proceedings of sixth IEEE international conference on automatic face and gesture recognition, pp 17–22

  192. 192.

    Junxia G, Ding X, Wang S, Youshou W (2010) Action and gait recognition from recovered 3-D human joints. IEEE Trans Syst Man Cybern Part B: Cybern 40(4):1021–1033.

    Article  Google Scholar 

  193. 193.

    Zhang X, Fan G (2010) Dual gait generative models for human motion estimation from a single camera. IEEE Tran Syst Man Cybern Part B Cybern 40(4):1034–1049.

    Article  Google Scholar 

  194. 194.

    Wang L, Ning H, Hu W, Tan T (2002) Gait recognition based on procrustes shape analysis. In: Proceeding of IEEE international conference on image processing (ICIP), pp 433–436

  195. 195.

    Shutler JD, Nixon MS (2006) Zernike velocity moments for sequence- based description of moving features. Image Vis Comput 24(4):343–356.

    Article  Google Scholar 

  196. 196.

    Shutler JD, Nixon MS, Harris CJ (2000) Statistical gait description via temporal moments. In: 4th IEEE southwest symposium on image analysis and interpretation, pp 291–295.

  197. 197.

    Veeraraghavan A, Chowdhury AR, Chellappa R (2004) Role of shape and kinematics in human movement analysis. In: IEEE computer society conference on computer vision and pattern recognition, pp I–I.

  198. 198.

    Yu CC, Heng CH, Fan KC (2014) A gait classification system using optical flow features. Journal of Information Science and Engineering 30:179–193

    Google Scholar 

  199. 199.

    Boulgouris NV, Plataniotis KN, Hatzinakos D (2006) Gait recognition using linear time normalization. Pattern Recogn 39(5):969–979.

    MATH  Article  Google Scholar 

  200. 200.

    Luo J, Zhang J, Zi C, Niu Y, Tian H, Xiu C (2015) Gait recognition using GEI and AFDEI. Int J Opt 215:763908.

    Article  Google Scholar 

  201. 201.

    Bashir K, Xiang T, Gong S (2009) Gait recognition using Gait Entropy Image. In: 3rd international conference on imaging for crime detection and prevention, pp 1–6.

  202. 202.

    Kusakunniran W, Wu Q, Zhang J, Li H (2012) Gait recognition across various walking speeds using higher order shape configuration based on a differential composition model. IEEE Trans Syst Man Cybern B Cybern 42(6):1654–1668.

    Article  Google Scholar 

  203. 203.

    Rida I, Almaadeed S, Bouridane A (2016) Gait recognition based on modified phase-only correlation. Signal Image Video Process 10(3):463–470.

    Article  Google Scholar 

  204. 204.

    Yu S, Tan D, Tan T (2006) Modelling the effect of view angle variation on appearance-based gait recognition. In: Asian conference on computer vision (ACCV), pp 807–816.

  205. 205.

    Kale A, Chowdhury AKR, Chellappa R (2003) Towards a view invariant gait recognition algorithm. In: IEEE conference on advanced video and signal based surveillance (AVSS), pp 143–150.

  206. 206.

    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):140–154.

    MathSciNet  MATH  Article  Google Scholar 

  207. 207.

    Bodor R, Drenner A, Fehr D, Masoud O, Papanikolopoulos N (2009) View-independent human motion classification using image-based reconstruction. Image Vis Comput 27:1194–1206.

    Article  Google Scholar 

  208. 208.

    Tang J, Luo J, Tjahjadi T, Guo F (2017) Robust arbitrary-view gait recognition based on 3D partial similarity matching. IEEE Trans Image Process 26(1):7–22.

    MathSciNet  MATH  Article  Google Scholar 

  209. 209.

    Chen X, Yang T, Jiaming X (2014) Cross-view gait recognition based on human walking trajectory. J Vis Commun Image Represent 25:1842–1855.

    Article  Google Scholar 

  210. 210.

    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 

  211. 211.

    Connie T, Goh MKO, Teoh ABJ (2017) A Grassmannian approach to address view change problem in gait recognition. IEEE Trans Cybern 47(6):1395–1408.

    Article  Google Scholar 

  212. 212.

    Xu W, Zhu C, Wang Z (2018) Multiview max-margin subspace learning for cross-view gait recognition. Pattern Recogn Lett 107:75–82.

    Article  Google Scholar 

  213. 213.

    Ji N, Sanchez V, Li C-T (2018) On view-invariant gait recognition: a feature selection solution. IET Biom 7(4):287–295.

    Article  Google Scholar 

  214. 214.

    Zhang Z, Chen J, Qiang W, Shao L (2018) GII representation-based cross-view gait recognition by discriminative projection with list-wise constraints. IEEE Trans Cybern 48(10):2935–2947.

    Article  Google Scholar 

  215. 215.

    Sharma H, Grover J (2018) Human identification based on gait recognition for multiple view angles. Int J Intell Robot Appl.

    Article  Google Scholar 

  216. 216.

    Sun J, Wang Y, Li J, Wan W, Cheng D, Zhang H (2018) View-invariant gait recognition based on kinect skeleton feature. Multimed Tools Appl 99:1–27.

    Article  Google Scholar 

  217. 217.

    Hossain MA, Makihara Y, Wang J, Yagi Y (2010) Clothing-invariant gait identification using part-based clothing categorization and adaptive weight control. Pattern Recogn 43:2281–2291.

    Article  Google Scholar 

  218. 218.

    Guan Y, Li C-T, Hu Y (2012) Robust clothing-invariant gait recognition. In: Eighth international conference on intelligent information hiding and multimedia signal processing, pp 321–324.

  219. 219.

    Islam MS, Islam MR, Akter MS, Hossain MA, Molla MKI (2013) Window Based clothing invariant gait recognition. In: 2nd international conference on advances in electrical engineering (ICAEE), pp 411–414.

  220. 220.

    Choudhury SD, Tjahjadi T (2015) Robust view-invariant multi-scale gait recognition. Pattern Recogn 48(3):798–811.

    Article  Google Scholar 

  221. 221.

    Rida I, Bouridane A, Marcialis GL, Tuveri P (2015) Improved human gait recognition. In: Proceeding international conference on image analysis and processing, pp 119–129

  222. 222.

    Yeoh T, Aguirre HE, Tanaka K (2016) Clothing-invariant gait recognition using convolutional neural network, pp 1–5.

  223. 223.

    Chaurasia P, Yogarajah P, Condell J, Prasad G (2017) Fusion of random walk and discrete fourier spectrum methods for gait recognition. IEEE Trans Hum-Mach Syst 47(6):751–762.

    Article  Google Scholar 

  224. 224.

    Yu S, Chen H, Wang Q, Shen L, Huang Y (2017) Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing 239:81–93.

    Article  Google Scholar 

  225. 225.

    Ghebleh A, Ebrahimi-Moghaddam M (2018) Clothing-invariant human gait recognition using an adaptive outlier detection method. Multimed Tools Appl 77:8237–8257.

    Article  Google Scholar 

  226. 226.

    Li X, Makihara Y, Chi X, Muramatsu D, Yagi Y, Ren M (2018) Gait energy response functions for gait recognition against various clothing and carrying status. Applied Sciences 8(8):1380.

    Article  Google Scholar 

  227. 227.

    Tsuji A, Makihara Y, Yagi Y (2010) Silhouette transformation based on walking speed for gait identification. In: IEEE computer society conference on computer vision and pattern recognition, pp 717–722.

  228. 228.

    Iwashita Y, Sakano H, Kurazume R (2015) Gait recognition robust to speed transition using mutual subspace method. In: International conference on image analysis and processing (ICIAP), pp 141–149.

  229. 229.

    Cho N, Yuille AL, Lee S (2012) Self-occlusion robust 3D human pose tracking from monocular image sequence. In: IEEE International conference on systems, man and cybernatics, pp 254–257

  230. 230.

    Roy A, Sural S, Mukherjee J, Rigoll G (2011) Occlusion detection and gait silhouette reconstruction from degraded scenes. Signal Image Video Process 5:415–430.

    Article  Google Scholar 

  231. 231.

    Gafurov D, Nekkenes E, Bours P (2007) Spoof attacks on gait authentication system. IEEE Trans Inf Forensics Secur 2(3):491–502.

    Article  Google Scholar 

  232. 232.

    Gafurov D (2007) Secularity analysis of impostor attempts with respect gender in gait biometrics. In: IEEE international conference on biometrics: theory, applications, and systems, pp 1–6.

  233. 233.

    Hadid A, Ghahramani M, Kellokumpu V, Pietikäinen M, Bustard J, Nixon M (2012) Can gait biometrics be spoofed?. In: 21st international conference on pattern recognition, pp 3280–3283

Download references

Author information



Corresponding author

Correspondence to Sakshi Arora.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Singh, J.P., Jain, S., Arora, S. et al. A Survey of Behavioral Biometric Gait Recognition: Current Success and Future Perspectives. Arch Computat Methods Eng 28, 107–148 (2021).

Download citation