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Static and dynamic hand gesture recognition system with deep convolutional levy flight whale optimization

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Abstract

The Hand gesture recognition-based research field plays a prominent role in the automated transformation of sign language and is the major source of communication among deaf people. During a reorganization of hand gestures, the background deduction cannot deal with sudden, drastic lighting changes leading to several inconsistencies. This method also requires relatively many parameters, which need to be selected intelligently. A novel Gabor Line Derivative Deep Convolution Neural Network-based Levy flight Whale optimization is introduced. Primarily pre-processing is done to diminish the computation complexity of processing red, green, and blue channel images. With the Gabor Line Derivative-based feature extraction technique, a relevant set of line features are extracted and subjected to the proposed optimization-driven deep learning algorithm. Deep learning approaches are quite popular in the recognition of HGIs, but choosing appropriate hyper-parameters is a complex problem. Additionally, the key problem associated with deep learning techniques is that the outcome of the accuracy measure attained is not much effective in the existing models. Thus, a novel Deep Convolution Neural Network based Levy flight Whale optimization is introduced in terms of categorizing dissimilar static and dynamic HGIs. The experimental analysis reveals that the proposed classifier performs better than other competitive existing methods through the performance matrices such as Precision, Accuracy, F1-score, Recall, specificity, Recognition time, FNR, FDR, loss, FPR, MCC, training time, and NPV. The combination of the proposed methods is enabled and attained an accuracy of about 97%. The implementation of this work is done in the python platform.

Highlights

  • A new approach is proposed to recognize hand gestures.

  • Due to the drastic lighting changes, a novel Gabor Line Derivative_ Deep Convolution Neural Network based Levy flight Whale optimization is proposed.

  • To solve the overfitting issues a novel Deep Convolution Neural Network based Levy flight Whale optimization is proposed.

  • The methods are used to recognize the hand gestures of deaf people.

  • Two methods are used to recognize the Hand gesture image (HGIs).

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Data availability

The total data collection is from the software simulation reports and the tools used by the authors. Real-world implementation is being worked out by the authors for appropriate permission to collect real-world data.

Code availability

Not applicable.

References

  1. Ahuja R, Jain D, Sachdeva D, Garg A, Rajput C (2019) Convolutional neural network based american sign language static hand gesture recognition. Int J Ambient Comput Intell (IJACI) 10(3):60–73

    Article  Google Scholar 

  2. Al-Hammadi M, Muhammad G, Abdul W, Alsulaiman M, Bencherif MA, Alrayes TS, Mathkour H, Mekhtiche MA (2020) Deep learning-based approach for sign language gesture recognition with efficient hand gesture representation. IEEE Access 8:192527–192542

    Article  Google Scholar 

  3. Al-Hammadi M, Muhammad G, Abdul W, Alsulaiman M, Hossain MS (2019) Hand gesture recognition using 3D-CNN model. IEEE Consum Electron Mag 9(1):95–101

    Article  Google Scholar 

  4. Ameur S, Khalifa AB, Bouhlel MS (2020) A novel hybrid bidirectional unidirectional LSTM network for dynamic hand gesture recognition with leap motion. Entertainment Comput 35:100373

    Article  Google Scholar 

  5. Banning WP (2013) Commercial broadcasting pioneer. Commercial Broadcasting Pioneer. Harvard University Press, Cambridge

  6. Bao P, Maqueda AI, del-Blanco CR, García N (2017) Tiny hand gesture recognition without localization via a deep convolutional network. IEEE Trans Consum Electron 63(3):251–257

  7. Benalcázar ME, Jaramillo AG, Zea A, Páez A, Andaluz VH (2017) Hand gesture recognition using machine learning and the Myo armband. In: 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, pp 1040–1044

  8. Bhuvaneshwari C, Manjunathan A (2020) Advanced gesture recognition system using long-term recurrent convolution network. Mater Today: Proc 21:731–733

    Google Scholar 

  9. Chakraborty C, Kishor A, Rodrigues JJ (2022) Novel Enhanced-Grey Wolf Optimization hybrid machine learning technique for biomedical data computation. Comput Electr Eng 99:107778

  10. Choi HR, Kim T (2018) Modified dynamic time warping based on direction similarity for fast gesture recognition. Math Probl Eng 2018:1–9

  11. Chung YL, Chung HY, Tsai WF (2020) Hand gesture recognition via image processing techniques and deep CNN. J Intell Fuzzy Syst 39(3):4405–4418

  12. De Smedt Q, Wannous H, Vandeborre JP (2019) Heterogeneous hand gesture recognition using 3D dynamic skeletal data. Comput Vis Image Underst 181:60–72

    Article  Google Scholar 

  13. Kane L, Khanna P (2019) Depth matrix and adaptive Bayes classifier based dynamic hand gesture recognition. Pattern Recognit Lett 120:24–30

    Article  Google Scholar 

  14. Khodabandelou G, Jung PG, Amirat Y, Mohammed S (2020) Attention-based gated recurrent unit for gesture recognition. IEEE Trans Autom Sci Eng 18(2):495–507

    Article  Google Scholar 

  15. Kishor A, Chakraborty C (2022) Artificial intelligence and internet of things based healthcare 4.0 monitoring system. Wirel Pers Commun 127(2):1615–31

  16. Kishor A, Chakraborty C, Jeberson W (2021) Reinforcement learning for medical information processing over heterogeneous networks. Multimedia Tools Appl 80(16):23983–24004

    Article  Google Scholar 

  17. Kumar M, Jindal MK, Kumar M (2022) Distortion, rotation and scale invariant recognition of hollow Hindi characters. Sādhanā 47(2):1–6

    Article  Google Scholar 

  18. Kumar M, Jindal MK, Kumar M (2021) A novel attack on monochrome and Greyscale Devanagari CAPTCHAs. Trans Asian Low-Resour Lang Inform Process 20(4):1–30

    Article  Google Scholar 

  19. Li G, Tang H, Sun Y, Kong J, Jiang G, Jiang D, Tao B, Xu S, Liu H (2019) Hand gesture recognition based on convolution neural network. Cluster Comput 22(2):2719–2729

    Article  Google Scholar 

  20. Liu F, Zeng W, Yuan C, Wang Q, Wang Y (2019) Kinect-based hand gesture recognition using trajectory information, hand motion dynamics and neural networks. Artif Intell Rev 52(1):563–583

    Article  Google Scholar 

  21. Nayak J, Naik B, Dash PB, Souri A, Shanmuganathan V (2021) Hyper-parameter tuned light gradient boosting machine using memetic firefly algorithm for hand gesture recognition. Appl Soft Comput 107:107478

    Article  Google Scholar 

  22. Ozcan T, Basturk A (2019) Transfer learning-based convolutional neural networks with heuristic optimization for hand gesture recognition. Neural Comput Appl 31(12):8955–8970

    Article  Google Scholar 

  23. Parvathy P, Subramaniam K, Venkatesan GP, Karthikaikumar P, Varghese J, Jayasankar T (2021) Development of hand gesture recognition system using machine learning. J Ambient Intell Humaniz Comput 12(6):6793–6800

    Article  Google Scholar 

  24. Pinto RF, Borges CD, Almeida AM, Paula IC (2019) Static hand gesture recognition based on convolutional neural networks. J Elect Comput Eng 2019:1–2

  25. Saboo S, Singha J, Laskar RH (2022) Dynamic hand gesture recognition using combination of two-level tracker and trajectory-guided features. Multimedia Systems 28(1):183–94

  26. Sharma S, Singh S (2021) Vision-based hand gesture recognition using deep learning for the interpretation of sign language. Expert Syst Appl 182:115657

    Article  Google Scholar 

  27. Skaria S, Al-Hourani A, Lech M, Evans RJ (2019) Hand-gesture recognition using two-antenna Doppler radar with deep convolutional neural networks. IEEE Sens J 19(8):3041–3048

    Article  Google Scholar 

  28. Smith JW, Thiagarajan S, Willis R, Makris Y, Torlak M (2021) Improved Static Hand Gesture classification on deep convolutional neural networks using novel sterile training technique. IEEE Access 9:10893–10902. https://doi.org/10.1109/ACCESS.2021.3051454

  29. Verma B, Choudhary A (2020) Grassmann manifold based dynamic hand gesture recognition using depth data. Multimedia Tools Appl 79(3):2213–2237

    Article  Google Scholar 

  30. Walugembe H, Phillips C, Requena-Carrion J, Timotijevic T (2021) Comparing dynamic hand rehabilitation gestures in leap motion using multi-dimensional dynamic time warping. IEEE Sens J 21(6):8002–8010. https://doi.org/10.1109/JSEN.2020.3047268

    Article  Google Scholar 

  31. Xia Z, Luomei Y, Zhou C, Xu F (2020) Multidimensional feature representation and learning for robust hand-gesture recognition on commercial millimeter-wave radar. IEEE Trans Geosci Remote Sens 59(6):4749–4764

    Article  Google Scholar 

  32. Zheng D, Zhao Y, Wang J (2004) August. Features extraction using a Gabor filter family. In Proceedings of the sixth Lasted International conference, Signal and Image processing, Hawaii

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Authors and Affiliations

  1. Department of Computer Science and Engineering, B.S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

    Subhashini S. & Revathi S.

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  1. Subhashini S.
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  2. Revathi S.
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Contributions

Author 1: Subhashini S.

He participated in the methodology, Conceptualization, Data collection, and writing the study.

Author 2: S Revathi.

He Performed the Analysis of the overall concept, writing, and editing.

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Correspondence to Subhashini S..

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Cite this article

S., S., S., R. Static and dynamic hand gesture recognition system with deep convolutional levy flight whale optimization. Multimed Tools Appl 83, 1559–1588 (2024). https://doi.org/10.1007/s11042-023-15397-8

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  • DOI: https://doi.org/10.1007/s11042-023-15397-8

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