Multi-level features fusion and selection for human gait recognition: an optimized framework of Bayesian model and binomial distribution

  • Habiba Arshad
  • Muhammad Attique KhanEmail author
  • Muhammad Sharif
  • Mussarat Yasmin
  • Muhammad Younus Javed
Original Article


A biometric classification system is utilized to judge the features of human expression by recognizing distinct parameters. Human Gait Recognition (HGR) is a current research area which is mostly used for various security applications such as video surveillance etc. HGR is also utilized in medical imaging for the investigation of several diseases such as Parkinson disease which is identified by gait features. Still, various challenges occur in this domain that affects system accuracies such as shoe type, change in angle, load carriage and change in walking speed. In this research, a new approach for HGR is proposed which is based on Quartile Deviation of Normal Distribution (QDoND) for human extraction and Bayesian model along with Binomial Distribution for features fusion and best features selection. Initially, in the pre-processing step, the most excellent channel is selected and its motion flow is estimated. The motion regions are extracted by QDoND that are later utilized for shape and texture feature extraction. Afterward, the extracted features are fused by a Bayesian model based on their similarity index. Finally, BDs based best features are selected and recognition is performed on the basis of best features using multi-class support vector machine. Four publicly and famous datasets are utilized for the evaluation of proposed system such as AVA multi-view gait (AVAMVG), CASIA A, CASIA B and CASIA C having an accuracy rate of 100%, 98.8%, 87.7%, and 91.6% respectively. The results reveal that the proposed method outperforms in contrast to existing methods.


HGR Human extraction Features fusion Features selection Recognition 



  1. 1.
    Akram T, Khan MA, Sharif M, Yasmin M (2018) Skin lesion segmentation and recognition using multichannel saliency estimation and M-SVM on selected serially fused features. J Amb Intell Hum Comput 2018:1–20Google Scholar
  2. 2.
    Al-Tayyan A, Assaleh K, Shanableh T (2017) Decision-level fusion for single-view gait recognition with various carrying and clothing conditions. Image Vis Comput 61:54–69CrossRefGoogle Scholar
  3. 3.
    Alotaibi M, Mahmood A (2017) Reducing covariate factors of gait recognition using feature selection and dictionary-based sparse coding. SIViP 11(6):1131–1138CrossRefGoogle Scholar
  4. 4.
    Arora P, Hanmandlu M, Srivastava S (2015) Gait based authentication using gait information image features. Pattern Recogn Lett 68:336–342CrossRefGoogle Scholar
  5. 5.
    Barron J, Fleet DJ, Beauchemin SS, Burkitt TA (1992) Performance of optical flow techniques. In: CVPRGoogle Scholar
  6. 6.
    Batchuluun G, Naqvi RA, Kim W, Park KR (2018) Body-movement-based human identification using convolutional neural network. Expert Syst Appl 101:56–77CrossRefGoogle Scholar
  7. 7.
    Binsaadoon AG, El-Alfy E-SM (2016) Kernel-based fuzzy local binary pattern for gait recognition. In: Modelling symposium (EMS), 2016, European, IEEEGoogle Scholar
  8. 8.
    Castro FM, Marín-Jimenez MJ, Medina-Carnicer R (2014) Pyramidal fisher motion for multiview gait recognition. In: Pattern recognition (ICPR), 2014 22nd international conference on, IEEEGoogle Scholar
  9. 9.
    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):751–762CrossRefGoogle Scholar
  10. 10.
    Chhatrala R, Jadhav DV (2016) Multilinear Laplacian discriminant analysis for gait recognition. IET Comput Vis 11(2):153–160CrossRefGoogle Scholar
  11. 11.
    Choudhury SD, Tjahjadi T (2016) Clothing and carrying condition invariant gait recognition based on rotation forest. Pattern Recogn Lett 80:1–7CrossRefGoogle Scholar
  12. 12.
    Dadashi F, Araabi BN, Soltanian-Zadeh H (2009) Gait recognition using wavelet packet silhouette representation and transductive support vector machines. In: Image signal processing, 2009. CISP’09. 2nd international congress on, IEEEGoogle Scholar
  13. 13.
    DeCann B, Ross A (2010) Gait curves for human recognition, backpack detection, and silhouette correction in a nighttime environment. In: Biometric technology for human identification VII, international society for optics and photonicsGoogle Scholar
  14. 14.
    Deng M, Wang C, Cheng F, Zeng W (2017) Fusion of spatial-temporal and kinematic features for gait recognition with deterministic learning. Pattern Recogn 67:186–200CrossRefGoogle Scholar
  15. 15.
    El-Alfy H, Mitsugami I, Yagi Y (2017) Gait recognition based on normal distance maps. IEEE Trans Cybern 48:1526–1539Google Scholar
  16. 16.
    Geng X, Wang L, Li M, Wu Q, Smith-Miles K (2007) Distance-driven fusion of gait and face for human identification in video. In: Image and vision computing conference, image and vision computing New ZealandGoogle Scholar
  17. 17.
    George AS, Roy E, Antony A, Job M (2017) An efficient gait recognition system for human identification using neural networks. Int J Innov Adv Comput Sci 6:76–83Google Scholar
  18. 18.
    Huang C-C, Hsu C-C, Liao H-Y, Yang S-H, Wang L-L, Chen S-Y (2016) Frontal gait recognition based on spatio-temporal interest points. J Chin Inst Eng 39(8):997–1002CrossRefGoogle Scholar
  19. 19.
    Khan MA, Akram T, Sharif M, Awais M, Javed K, Ali H, Saba T (2018) CCDF: automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features. Comput Electron Agric 155:220–236CrossRefGoogle Scholar
  20. 20.
    Khan MA, Akram T, Sharif M, Javed MY, Muhammad N, Yasmin M (2018) An implementation of optimized framework for action classification using multilayers neural network on selected fused features. Pattern Anal Appl 2018:1–21Google Scholar
  21. 21.
    Khan MA, Sharif M, Javed MY, Akram T, Yasmin M, Saba T (2017) License number plate recognition system using entropy-based features selection approach with SVM. IET Image Proc 12(2):200–209CrossRefGoogle Scholar
  22. 22.
    Khan MH, Li F, Farid MS, Grzegorzek M (2017) Gait recognition using motion trajectory analysis. In: International conference on computer recognition systems. Springer, BerlinGoogle Scholar
  23. 23.
    Kumar HM, Nagendraswamy H (2014) LBP for gait recognition: a symbolic approach based on GEI plus RBL of GEI. In: Electronics and communication systems (ICECS), 2014 international conference on, IEEEGoogle Scholar
  24. 24.
    Kusakunniran W, Wu Q, Zhang J, Li H (2011) Pairwise shape configuration-based psa for gait recognition under small viewing angle change. In: Advanced video and signal-based surveillance (AVSS), 2011 8th IEEE international conference on, IEEEGoogle Scholar
  25. 25.
    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 Part B (Cybernetics) 42(6):1654–1668CrossRefGoogle Scholar
  26. 26.
    Lee H, Hong S, Kim E (2008) An efficient gait recognition based on a selective neural network ensemble. Int J Imaging Syst Technol 18(4):237–241CrossRefGoogle Scholar
  27. 27.
    Liao R, Cao C, Garcia EB, Yu S, Huang Y (2017) Pose-based temporal-spatial network (PTSN) for gait recognition with carrying and clothing variations. In: Chinese conference on biometric recognition. Springer, BerlinGoogle Scholar
  28. 28.
    Liaqat A, Khan MA, Shah JH, Sharif M, Yasmin M, Fernandes SL (2018) Automated ulcer and bleeding classification from WCE images using multiple features fusion and selection. J Mech Med Biol 18:50038CrossRefGoogle Scholar
  29. 29.
    Liu L-F, Jia W, Zhu Y-H (2009) Gait recognition using hough transform and principal component analysis. In: International conference on intelligent computing. Springer, BerlinGoogle Scholar
  30. 30.
    López-Fernández D, Madrid-Cuevas F, Carmona-Poyato A, Marín-Jiménez M, Salinas MR (2013) The AVA multi-view dataset for gait recognition (AVAMVG). In: 2nd workshop activity monitoring by multiple distributed sensing (AMMDS). ICPRGoogle Scholar
  31. 31.
    López-Fernández D, Madrid-Cuevas FJ, Carmona-Poyato Á, Marín-Jiménez MJ, Muñoz-Salinas R (2014) The AVA multi-view dataset for gait recognition. In: International workshop on activity monitoring by multiple distributed sensing. Springer, BerlinGoogle Scholar
  32. 32.
    López-Fernández D, Madrid-Cuevas FJ, Carmona-Poyato A, Marín-Jiménez MJ, Muñoz-Salinas R, Medina-Carnicer R (2016) independent gait recognition through morphological descriptions of 3D human reconstructions. Image Vis Comput 48:1–13CrossRefGoogle Scholar
  33. 33.
    López-Fernández D, Madrid-Cuevas FJ, Carmona-Poyato A, Muñoz-Salinas R, Medina-Carnicer R (2015) Entropy volumes for viewpoint-independent gait recognition. Mach Vis Appl 26(7–8):1079–1094CrossRefGoogle Scholar
  34. 34.
    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 38:396–406CrossRefGoogle Scholar
  35. 35.
    Ma S, Ma H, Xu Y, Li S, Lv C, Zhu M (2018) “A Low-Light Sensor Image Enhancement Algorithm Based on HSI Color Model. " Sensors 18(10):3583CrossRefGoogle Scholar
  36. 36.
    Marín-Jiménez MJ, Castro FM, Carmona-Poyato Á, Guil N (2015) On how to improve tracklet-based gait recognition systems. Pattern Recogn Lett 68:103–110CrossRefGoogle Scholar
  37. 37.
    Martinez-Hernandez U, Dehghani-Sanij AA (2018) Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors. Neural Netw 102:107–119CrossRefGoogle Scholar
  38. 38.
    Mogan JN, Lee CP, Lim KM, Tan AW (2017) Gait recognition using binarized statistical image features and histograms of oriented gradients. In: Robotics, automation and sciences (ICORAS), 2017 international conference on, IEEEGoogle Scholar
  39. 39.
    Mogan JN, Lee CP, Tan AW (2017) Gait recognition using temporal gradient patterns. In: Information and communication technology (ICoIC7), 2017 5th international conference on, IEEEGoogle Scholar
  40. 40.
    Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59CrossRefGoogle Scholar
  41. 41.
    Ortells J, Mollineda RA, Mederos B, Martín-Félez R (2017) Gait recognition from corrupted silhouettes: a robust statistical approach. Mach Vis Appl 28(1–2):15–33CrossRefGoogle Scholar
  42. 42.
    Portillo-Portillo J, Leyva R, Sanchez V, Sanchez-Perez G, Perez-Meana H, Olivares-Mercado J, Toscano-Medina K, Nakano-Miyatake M (2018) A view-invariant gait recognition algorithm based on a joint-direct linear discriminant analysis. Appl Intell 48(5):1200–1217Google Scholar
  43. 43.
    Raza M, Sharif M, Yasmin M, Khan MA, Saba T, Fernandes SL (2018) Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning. Future Gen Comput Syst 88:28–39CrossRefGoogle Scholar
  44. 44.
    Rida I, Bouridane A, Marcialis GL, Tuveri P (2015) Improved human gait recognition. In: International conference on image analysis and processing. Springer, BerlinGoogle Scholar
  45. 45.
    Shaikh SH, Saeed K, Chaki N (2014) Gait recognition using partial silhouette-based approach. In: Signal processing and integrated networks (SPIN), 2014 international conference on, IEEEGoogle Scholar
  46. 46.
    Sharif M, Khan MA, Faisal M, Yasmin M, Fernandes SL (2018) A framework for offline signature verification system: best features selection approach. Pattern Recogn Lett. CrossRefGoogle Scholar
  47. 47.
    Sharif M, Khan MA, Rashid M, Yasmin M, Afza F, Tanik UJ (2018) Deep CNN and geometric features-based gastrointestinal tract diseases detection and classification from wireless capsule endoscopy images. J Exp Theor Artif Intell. CrossRefGoogle Scholar
  48. 48.
    Sharif M, Tanvir U, Munir EU, Khan MA, Yasmin M (2018) Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. J Amb Intell Hum Comput 2018:1–20Google Scholar
  49. 49.
    Siddiqui S, Khan MA, Bashir K, Sharif M, Azam F, Javed MY (2018) Human action recognition: a construction of codebook by discriminative features selection approach. Int J Appl Pattern Recogn 5(3):206–228CrossRefGoogle Scholar
  50. 50.
    Sokolova A, Konushin A (2017) Pose-based deep gait recognition. arXiv preprint arXiv:1710.06512Google Scholar
  51. 51.
    Tan D, Huang K, Yu S, Tan T (2006) Efficient night gait recognition based on template matching. In: Pattern recognition, 2006. ICPR 2006. 18th international conference on, IEEEGoogle Scholar
  52. 52.
    Tan D, Huang K, Yu S, Tan T (2007) Orthogonal diagonal projections for gait recognition. In: Image processing, 2007. In: ICIP 2007. IEEE international conference on, IEEEGoogle Scholar
  53. 53.
    Tan D, Huang K, Yu S, Tan T (2007) Uniprojective features for gait recognition. In: International conference on biometrics. Springer, BerlinGoogle Scholar
  54. 54.
    Tan D, Yu S, Huang K, Tan T (2007) Walker recognition without gait cycle estimation. In: International conference on biometrics. Springer, BerlinGoogle Scholar
  55. 55.
    Wang L, Tan T, Ning H, Hu W (2003) Silhouette analysis-based gait recognition for human identification. IEEE Trans Pattern Anal Mach Intell 25(12):1505–1518CrossRefGoogle Scholar
  56. 56.
    Wang Y, Shi F, Cao L, Dey N, Wu Q, Ashour AS, Sherratt S, Rajinikanth V, Wu L (2018) Morphological segmentation analysis and texture-based support vector machines classification on mice liver fibrosis microscopic images. Curr Bioinform. CrossRefGoogle Scholar
  57. 57.
    Wu Q, Wang L, Geng X, Li M, He S (2007) Dynamic biometrics fusion at feature level for video-based human recognition. In: Image and vision computing conference, image and vision computing New ZealandGoogle Scholar
  58. 58.
    Xue Z, Li SZ, Lu J, Teoh EK (2000) Bayesian model for extracting facial features. In: Sixth international conference on control, automation, robotics & Vision, ICARCV 2000, Dec., SingaporeGoogle Scholar
  59. 59.
    Yu S, Chen H, Wang Q, Shen L, Huang Y (2017) Invariant feature extraction for gait recognition using only one uniform model. Neurocomputing 239:81–93CrossRefGoogle Scholar
  60. 60.
    Yu S, Tan D, Tan T (2006) A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. In: Pattern recognition, 2006. ICPR 2006. 18th international conference on, IEEEGoogle Scholar
  61. 61.
    Zeng W, Wang C (2016) View-invariant gait recognition via deterministic learning. Neurocomputing 175:324–335CrossRefGoogle Scholar
  62. 62.
    Zhang E, Zhao Y, Xiong W (2010) Active energy image plus 2DLPP for gait recognition. Sig Process 90(7):2295–2302zbMATHCrossRefGoogle Scholar
  63. 63.
    Zhou N, Wang Y, Gong L, He H, Wu J (2011) Novel single-channel color image encryption algorithm based on chaos and fractional Fourier transform. Opt Commun 284(12):2789–2796CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Habiba Arshad
    • 1
  • Muhammad Attique Khan
    • 2
    Email author
  • Muhammad Sharif
    • 1
  • Mussarat Yasmin
    • 1
  • Muhammad Younus Javed
    • 2
  1. 1.Department of Computer ScienceCOMSATS University IslamabadIslamabadPakistan
  2. 2.Department of Computer Science and EngineeringHITEC UniversityTaxilaPakistan

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