Skip to main content
SpringerLink
Log in

Proposed methodology for gait recognition using generative adversarial network with different feature selectors

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Today, investigating gait recognition as a biometric technology has become necessary, especially after the COVID-19 pandemic broke out in the world. This paper proposes a deep structure procedure for precise human identification from the walking manner quickly, as each human has a unique way of walking. The proposed generative adversarial network (GAN) generates gait images from pedestrians of the CASIA and OU-ISIR gait datasets for normalizing the number of frames in each class in both datasets. Then, we extract the gait features from the normalized datasets by the pretrained convolutional neural network (CNN) models; AlexNet, Inception, VGG16, VGG19, ResNet, and Xception, and balance them using the synthetic minority oversampling technique (SMOTE) to enhance the recognition results. Several feature selectors are selecting the best features, including particle swarm optimization (PSO), grey wolf optimization (GWO), Chi-square, and genetic models. Finally, the proposed deep neural network recognizes the gait images precisely. Several performance assessment measures were generated to assess the model's quality, including accuracy, precision, sensitivity, specificity, false negative rate (FNR), intersection of union (IoU), and time. Experimental results showed that the inception model with genetic feature selector performed better in all used datasets than the current studies and was more robust against any change, achieving 99.3%, 99.1%, and 99.09% accuracy in identifying CASIA-B, CASIA-A, and OU-ISIR datasets, respectively in low time.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Algorithm 1
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Algorithm 2
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

Data availability

The data that support the findings of this study are available within this article.

References

  1. Priesnitz J, Huesmann R, Rathgeb C, Buchmann N, Busch C (2022) Mobile contactless fingerprint recognition: implementation, performance and usability aspects. Sensors 22(3):792

    Google Scholar 

  2. Du H, Shi H, Zeng D, Zhang X-P, Mei T (2022) The elements of end-to-end deep face recognition: a survey of recent advances. ACM Comput Surv (CSUR) 54(10s):1–42

    Google Scholar 

  3. Brue CR, Dukes MW, Masotti M, Holmgren R, Meade TJ (2022) Functional disruption of Gli1-DNA recognition via a cobalt (III) complex. ChemMedChem 17(8):e202200025

    Google Scholar 

  4. Cao Z, Huang J, He X, Zong Z (2022) BND-VGG-19: a deep learning algorithm for COVID-19 identification utilizing X-ray images. Knowl Based Syst, p 110040

  5. Ghosh R (2022) A faster R-CNN and recurrent neural network based approach of gait recognition with and without carried objects. Exp Syst Appl, p 117730

  6. Li H et al (2022) GaitSlice: a gait recognition model based on spatio-temporal slice features. Pattern Recogn 124:108453

    Google Scholar 

  7. Han F, Li X, Zhao J, Shen F (2022) A unified perspective of classification-based loss and distance-based loss for cross-view gait recognition. Pattern Recogn 125:108519

    Google Scholar 

  8. Bird JJ, Barnes CM, Manso LJ, Ekárt A, Faria DR (2022) Fruit quality and defect image classification with conditional GAN data augmentation. Sci Hortic 293:110684

    Google Scholar 

  9. Sadiq MT, Aziz MZ, Almogren A, Yousaf A, Siuly S, Rehman AU (2022) Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework. Comput Biol Med 143:105242

    Google Scholar 

  10. Dablain D, Krawczyk B, Chawla NV (2022) DeepSMOTE: fusing deep learning and SMOTE for imbalanced data. IEEE Transact Neural Netw Learn Syst

  11. Bansal SR, Wadhawan S, Goel R (2022) mRMR-PSO: a hybrid feature selection technique with a multiobjective approach for sign language recognition. Arab J Sci Eng pp 1–16

  12. Hu J et al (2022) Chaotic diffusion-limited aggregation enhanced grey wolf optimizer: insights, analysis, binarization, and feature selection. Int J Intell Syst 37(8):4864–4927

    Google Scholar 

  13. Mengash HA et al (2022) Smart cities-based improving atmospheric particulate matters prediction using chi-square feature selection methods by employing machine learning techniques. Appl Artif Intell 36(1):2067647

    Google Scholar 

  14. Shreem SS, Turabieh H, Al Azwari S, Baothman F (2022) Enhanced binary genetic algorithm as a feature selection to predict student performance. Soft Comput 26(4):1811–1823

    Google Scholar 

  15. Zhang Z, Wang Z, Lei H, Gu W (2022) Gait phase recognition of lower limb exoskeleton system based on the integrated network model. Biomed Signal Process Control 76:103693

    Google Scholar 

  16. Liao R, Li Z, Bhattacharyya SS, York G (2022) PoseMapGait: a model-based gait recognition method with pose estimation maps and graph convolutional networks. Neurocomputing 501:514–528

    Google Scholar 

  17. Gheisari M et al. (2023) Deep learning: applications, architectures, models, tools, and frameworks: a comprehensive survey. CAAI Transact Intell Technol

  18. Cai S, Chen D, Fan B, Du M, Bao G, Li G (2023) Gait phases recognition based on lower limb sEMG signals using LDA-PSO-LSTM algorithm. Biomed Signal Process Control 80:104272

    Google Scholar 

  19. Asif M, Tiwana MI, Khan US, Ahmad MW, Qureshi WS, Iqbal J (2022) Human gait recognition subject to different covariate factors in a multi-view environment. Result Eng 15:100556

    Google Scholar 

  20. Song X, Huang Y, Shan C, Wang J, Chen Y (2022) Distilled light GaitSet: towards scalable gait recognition. Pattern Recogn Lett 157:27–34

    Google Scholar 

  21. Wang L, Chen J, Liu Y (2022) Frame-level refinement networks for skeleton-based gait recognition. Comput Vis Image Underst 222:103500

    Google Scholar 

  22. Xu J, Li H, Hou S (2022) Autoencoder-guided GAN for minority-class cloth-changing gait data generation. Digital Signal Process, p 103608

  23. Rashmi M, Guddeti RMR (2022) Human identification system using 3D skeleton-based gait features and LSTM model. J Vis Commun Image Represent 82:103416

    Google Scholar 

  24. Parashar A, Parashar A, Shekhawat RS (2022) A robust covariate-invariant gait recognition based on pose features. IET Biom

  25. Saleh AM, Hamoud T (2021) Analysis and best parameters selection for person recognition based on gait model using CNN algorithm and image augmentation. J Big Data 8(1):1–20

    Google Scholar 

  26. Shui-Hua W, Khan MA, Govindaraj V, Fernandes SL, Zhu Z, Yu-Dong Z (2022) Deep rank-based average pooling network for COVID-19 recognition. Comput Mater Continua, pp 2797–2813

  27. Liu X, You Z, He Y, Bi S, Wang J (2022) Symmetry-driven hyper feature GCN for skeleton-based gait recognition. Pattern Recogn p 108520

  28. 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):1505–1518

    Google Scholar 

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

  30. 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'06), vol 3, pp 1000–1003, IEEE

  31. Makihara Y et al (2012) The OU-ISIR gait database comprising the treadmill dataset. IPSJ Transact Comput Vis Appl 4:53–62

    Google Scholar 

  32. Yousef RN, Khalil AT, Samra AS, Ata MM (2023) Model-based and model-free deep features fusion for high performed human gait recognition. J Supercomput, pp 1–38

  33. 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 Transact Comput Vis Appl 10(1):1–14

    Google Scholar 

  34. Donon Y, Paringer R, Kupriyanov A (2020) Image normalization for blurred image matching. In: CEUR workshop proceedings pp 127–131

  35. Puttagunta M, Subban R (2022) A novel COVID-19 detection model based on DCGAN and deep transfer learning. Procedia Comput Sci 204:65–72

    Google Scholar 

  36. An Y, Wang M, Chen L, Ji Z (2022) DCGAN-based symmetric encryption end-to-end communication systems. AEU Int J Electron Commun 154:154297

    Google Scholar 

  37. Zhang D, Ning Z, Yang B, Wang T, Ma Y (2022) Fault diagnosis of permanent magnet motor based on DCGAN-RCCNN. Energy Rep 8:616–626

    Google Scholar 

  38. Guan S, Lu H, Zhu L, Fang G (2022) AFE-CNN: 3D skeleton-based action recognition with action feature enhancement. arXiv:2208.03444

  39. Han J, Zhang D, Cheng G, Liu N, Xu D (2018) Advanced deep-learning techniques for salient and category-specific object detection: a survey. IEEE Signal Process Mag 35(1):84–100

    Google Scholar 

  40. Minu M, Canessane RA (2022) Deep learning-based aerial image classification model using inception with residual network and multilayer perceptron. Microprocess Microsyst 95:104652

    Google Scholar 

  41. Durga BK, Rajesh V (2022) A ResNet deep learning based facial recognition design for future multimedia applications. Comput Electr Eng 104:108384

    Google Scholar 

  42. Zhu Y, JiaYI H, Li Y, Li W (2022) Image identification of cashmere and wool fibers based on the improved Xception network. J King Saud Univ Comput Inf Sci 34(10):9301–9310

    Google Scholar 

  43. Arafa A, El-Fishawy N, Badawy M, Radad M (2022) RN-SMOTE: reduced noise SMOTE based on DBSCAN for enhancing imbalanced data classification. J King Saud Univ Comput Inf Sci 34(8):5059–5074

    Google Scholar 

  44. Lim S-J (2022) Hybrid image embedding technique using steganographic signcryption and IWT-GWO methods. Microprocess Microsyst 95:104688

    Google Scholar 

  45. Dokala JKK, Mohamed MR, Kumarasamy S, Kurukuri P (2022) A new meta-heuristic optimization algorithm based MPPT control technique for PV System under diverse partial shading conditions

  46. Gu Q, Li X, Jiang S (2019) Hybrid genetic grey wolf algorithm for large-scale global optimization. Complexity vol 2019

  47. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of ICNN'95-international conference on neural networks, vol 4, pp 1942–1948: IEEE

  48. Ibrahim A, Noshy M, Ali HA, Badawy M (2020) PAPSO: a power-aware VM placement technique based on particle swarm optimization. IEEE Access 8:81747–81764

    Google Scholar 

  49. Pramanik R, Sarkar S, Sarkar R (2022) An adaptive and altruistic PSO-based deep feature selection method for Pneumonia detection from chest X-rays. Appl Soft Comput 128:109464

    Google Scholar 

  50. Sadoughi F, Ghaderzadeh M (2014) A hybrid particle swarm and neural network approach for detection of prostate cancer from benign hyperplasia of prostate,. In: e-health–for continuity of care: IOS Press, pp 481–485

  51. Singh A, Sharma A, Rajput S, Bose A, Hu X (2022) An Investigation on Hybrid Particle Swarm Optimization Algorithms for Parameter Optimization of PV Cells. Electronics 11(6):909

    Google Scholar 

  52. Kunhare N, Tiwari R, Dhar J (2022) Intrusion detection system using hybrid classifiers with meta-heuristic algorithms for the optimization and feature selection by genetic algorithm. Comput Electr Eng 103:108383

    Google Scholar 

  53. Eligüzel N, Çetinkaya C, Dereli T (2022) A novel approach for text categorization by applying hybrid genetic bat algorithm through feature extraction and feature selection methods. Expert Syst Appl 202:117433

    Google Scholar 

  54. Wutzl B, Leibnitz K, Rattay F, Kronbichler M, Murata M, Golaszewski SM (2019) Genetic algorithms for feature selection when classifying severe chronic disorders of consciousness. PLoS ONE 14(7):e0219683

    Google Scholar 

  55. Saad M, El-Moursy A, Alfawaz O, Alnajjar K, Abdallah S (2022) Wireless link scheduling via parallel genetic algorithm. Concurr Comput Pract Exp 34(6):e6783

    Google Scholar 

  56. Thaseen IS, Kumar CA (2017) Intrusion detection model using fusion of chi-square feature selection and multi class SVM. J King Saud Univ Comput Inf Sci 29(4):462–472

    Google Scholar 

  57. Trivedi SK (2020) A study on credit scoring modeling with different feature selection and machine learning approaches. Technol Soc 63:101413

    Google Scholar 

  58. Sun L, Zou B, Fu S, Chen J, Wang F (2019) Speech emotion recognition based on DNN-decision tree SVM model. Speech Commun 115:29–37

    Google Scholar 

  59. Ghosh A, Sufian A, Sultana F, Chakrabarti A, De D (2020) Fundamental concepts of convolutional neural network. In: Recent trends and advances in artificial intelligence and Internet of Things: Springer, 2020, pp 519–567

  60. Banerjee K, Gupta RR, Vyas K, Mishra B (2020) Exploring alternatives to softmax function. arXiv:2011.11538

  61. M. Onim et al. (2022) BLPnet: a new DNN model and Bengali OCR engine for automatic license plate recognition. arXiv:2202.12250

  62. Chakraborty M, Kumawat HC, Dhavale SV (2022) Application of DNN for radar micro-doppler signature-based human suspicious activity recognition. Pattern Recogn Lett 162:1–6

    Google Scholar 

  63. Ata MM, Francies ML, Mohamed MA (2022) A robust optimized convolutional neural network model for human activity recognition using sensing devices. Concurr Comput Pract Exp 34(17):e6964

    Google Scholar 

  64. El Gannour O et al (2021) Concatenation of pre-trained convolutional neural networks for enhanced COVID-19 screening using transfer learning technique. Electronics 11(1):103

    Google Scholar 

  65. Grandini M, Bagli E, Visani G (2020) Metrics for multi-class classification: an overview. arXiv:2008.05756

  66. Ata M, Yousef R, Karim F, Khafaga D (2023) An improved deep structure for accurately brain tumor recognition. Comput Syst Sci Eng 46:1597–1616

    Google Scholar 

  67. Shreffler J, Huecker MR (2020) Diagnostic testing accuracy: sensitivity, specificity, predictive values and likelihood ratios

  68. Krstinić D, Braović M, Šerić L, Božić-Štulić D (2020) Multi-label classifier performance evaluation with confusion matrix. Comput Sci Inf Technol 10:1–14

    Google Scholar 

  69. A. Bhandari (2020) Everything you should know about confusion matrix for machine learning. Analytics Vidhya

  70. Lei L, Ramdas A, Fithian W (2021) A general interactive framework for false discovery rate control under structural constraints. Biometrika 108(2):253–267

    MathSciNet  Google Scholar 

  71. Kanji JN et al (2021) False negative rate of COVID-19 PCR testing: a discordant testing analysis. Virol J 18(1):1–6

    Google Scholar 

  72. D. Müller, I. Soto-Rey, and F. Kramer (2022) Towards a guideline for evaluation metrics in medical image segmentation. arXiv preprint arXiv:2202.05273

  73. Raksa ARA, Padukawan F, Aji KK, Alamsyah MR, Octaviyani S, Laksana EA (2022) Wall-following robot navigation classification using deep learning with sparse categorical crossentropy loss function. Central Asia Caucasus 23:2022

    Google Scholar 

  74. Rifaat N, Ghosh UK, Sayeed A (2022) Accurate gait recognition with inertial sensors using a new FCN-BiLSTM architecture. Comput Electr Eng 104:108428

    Google Scholar 

  75. Bukhari M et al (2020) An efficient gait recognition method for known and unknown covariate conditions. IEEE Access 9:6465–6477

    Google Scholar 

  76. H. Guo et al. (2020) Gait recognition based on the feature extraction of Gabor filter and linear discriminant analysis and improved local coupled extreme learning machine. Math Probl Eng 2020

  77. "CASIA gait dataset," http://www.cbsr.ia.ac.cn/users/szheng/?page_id=71.

  78. Uddin M et al (2018) The ou-isir large population gait database with real-life carried object and its performance evaluation. IPSJ Transact Comput Vis Appl 10(1):1–11

    Google Scholar 

Download references

Acknowledgements

The authors declare that there is no conflict of interest regarding the manuscript.

Funding

There is no funding to declare regarding this manuscript.

Author information

Authors and Affiliations

  1. Delta Higher Institute for Engineering and Technology, Mansoura, Egypt

    Reem N. Yousef

  2. Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt

    Abeer T. Khalil & Ahmed S. Samra

  3. School of Computational Sciences and Artificial Intelligence (CSAI), Zewail City of Science and Technology, October Gardens, 6th of October City, Giza, 12578, Egypt

    Mohamed Maher Ata

  4. Department of Communications and Electronics Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, Egypt

    Mohamed Maher Ata

Authors
  1. Reem N. Yousef
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Abeer T. Khalil
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ahmed S. Samra
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohamed Maher Ata
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohamed Maher Ata.

Ethics declarations

Conflict of interest

We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yousef, R.N., Khalil, A.T., Samra, A.S. et al. Proposed methodology for gait recognition using generative adversarial network with different feature selectors. Neural Comput & Applic 36, 1641–1663 (2024). https://doi.org/10.1007/s00521-023-09154-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-09154-z

Keywords

Search

Navigation