Detection of hand gestures with human computer recognition by using support vector machine

Sura Abdulmunem Mohammed Al-Juboori, Hissah Almutairi, Rasha Almajed, Amer Ibrahim, Hassan Muwafaq Gheni

Abstract


Many applications, such as interactive data analysis and sign detection, can benefit from hand gesture recognition. We offer a low-cost approach based on human-computer interaction for predicting hand movements in real time. Our technique involves using a color glove to train a random forest classifier and then predicting a naked hand at the pixel level. Our algorithm anticipates all pixels at a rate of around 3 frames per second and is unaffected by differences in the surroundings. It's also been proven that HCI-based data augmentation is more effective than any other way for enhancing interactive data. In addition, the augmentation experiment was carried out on multiple subsets of the original hand skeleton sequence dataset, each with a different number of classes, as well as on the entire dataset. On practically all subsets, the proposed base architecture improved classification accuracy. When the entire dataset was used, there was even a modest improvement. Correct identification could be regarded as a quality indicator. The best accuracy score was 94.02 percent for the HCI-model with support vector machine (SVM) classifier.

Full Text:

PDF

References


D.-S. Tran, N.-H. Ho, H.-J. Yang, S.-H. Kim, and G. S. Lee, “Real-time virtual mouse system using RGB-D images and fingertip detection,” Multimedia Tools and Applications, vol. 80, no. 7, pp. 10473–10490, Nov. 2020.

J. Schulte, M. Kocherovsky, N. Paul, M. Pleune, and C.-J. Chung, “Autonomous Human-Vehicle Leader-Follower Control Using Deep-Learning-Driven Gesture Recognition,” Vehicles, vol. 4, no. 1, pp. 243–258, Mar. 2022.

G. Benitez-Garcia, L. Prudente-Tixteco, L. C. Castro-Madrid, R. Toscano-Medina, J. Olivares-Mercado, G. Sanchez-Perez, and L. J. G. Villalba, “Improving Real-Time Hand Gesture Recognition with Semantic Segmentation,” Sensors, vol. 21, no. 2, p. 356, Jan. 2021.

K. Yang, M. Xu, X. Yang, R. Yang, and Y. Chen, “A Novel EMG-Based Hand Gesture Recognition Framework Based on Multivariate Variational Mode Decomposition,” Sensors, vol. 21, no. 21, p. 7002, Oct. 2021.

Y. Dong, J. Liu, and W. Yan, “Dynamic Hand Gesture Recognition Based on Signals From Specialized Data Glove and Deep Learning Algorithms,” IEEE Transactions on Instrumentation and Measurement, vol. 70, pp. 1–14, 2021.

K. Zhang and F. Chen, “Research on sEMG Gesture Recognition Based on Hybrid Dilated Convolutional Neural Network Combining Bidirectional Gated Recurrent Unit And Attention Mechanism,” 2021 China Automation Congress (CAC), Oct. 2021.

D. Bhavana, K. Kishore Kumar, M. Bipin Chandra, P. V. Sai Krishna Bhargav, D. Joy Sanjana, and G. Mohan Gopi, “Hand Sign Recognition using CNN,” International Journal of Performability Engineering, vol. 17, no. 3, p. 314, 2021.

A. Mujahid, M. J. Awan, A. Yasin, M. A. Mohammed, R. Damaševičius, R. Maskeliūnas, and K. H. Abdulkareem, “Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model,” Applied Sciences, vol. 11, no. 9, p. 4164, May 2021.

M. Murugeswari and S. Veluchamy, “Hand gesture recognition system for real-time application,” 2014 IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1220-1225 , May 2014.

J. Li et al., “Internet of things assisted condition-based support for smart manufacturing industry using learning technique,” Comput. Intell., vol. 36, no. 4, pp. 1737–1754, 2020, doi: 10.1111/coin.12319.

I. Al_barazanchi, Z. A. Jaaz, H. H. Abbas, and H. R. Abdulshaheed, “Practical application of iot and its implications on the existing software,” Int. Conf. Electr. Eng. Comput. Sci. Informatics, vol. 2020-Octob, no. October, pp. 10–14, 2020, doi: 10.23919/EECSI50503.2020.9251302.

H. D. Nguyen, Y. C. Kim, S. H. Kim, and I. S. Na, “A method for fingertips detection using RGB-D image and convolution neural network,” 2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), Jul. 2017.

P. Sun, W. Zhang, H. Wang, S. Li, and X. Li, “Deep RGB-D Saliency Detection with Depth-Sensitive Attention and Automatic Multi-Modal Fusion,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2021.

M. Al-Hammadi, G. Muhammad, W. Abdul, M. Alsulaiman, and M. S. Hossain, “Hand Gesture Recognition Using 3D-CNN Model,” IEEE Consumer Electronics Magazine, vol. 9, no. 1, pp. 95–101, Jan. 2020.

T. Mantecón, C. R. del-Blanco, F. Jaureguizar, and N. García, “A real-time gesture recognition system using near-infrared imagery,” PLOS ONE, vol. 14, no. 10, p. e0223320, Oct. 2019.

H. Tao et al., “A Newly Developed Integrative Bio-Inspired Artificial Intelligence Model for Wind Speed Prediction,” IEEE Access, vol. 8, pp. 83347–83358, 2020, doi: 10.1109/ACCESS.2020.2990439.

P. Neto, D. Pereira, J. N. Pires, and A. P. Moreira, “Real-time and continuous hand gesture spotting: An approach based on artificial neural networks,” 2013 IEEE International Conference on Robotics and Automation, May 2013.

A. Banerjee, A. Ghosh, K. Bharadwaj, and H. Saikia, “Mouse Control using a Web Camera based on Colour Detection,” International Journal of Computer Trends and Technology, vol. 9, no. 1, pp. 15–20, Mar. 2014.

U. Beyaztas, S. Q. Salih, K. W. Chau, N. Al-Ansari, and Z. M. Yaseen, “Construction of functional data analysis modeling strategy for global solar radiation prediction: application of cross-station paradigm,” Eng. Appl. Comput. Fluid Mech., vol. 13, no. 1, pp. 1165–1181, 2019, doi: 10.1080/19942060.2019.1676314.

A. Fossati, J. Gall, H. Grabner, X. Ren, and K. Konolige, Eds., “Consumer Depth Cameras for Computer Vision,” Advances in Computer Vision and Pattern Recognition, 2013.

M. M. Islam, M. R. Islam, and M. S. Islam, “An Efficient Human Computer Interaction through Hand Gesture Using Deep Convolutional Neural Network,” SN Computer Science, vol. 1, no. 4, Jun. 2020.

Z. Yang and X. Zheng, “Hand Gesture Recognition Based on Trajectories Features and Computation-Efficient Reused LSTM Network,” IEEE Sensors Journal, vol. 21, no. 15, pp. 16945–16960, Aug. 2021.

A. S. Abdullah, M. A. Abed, and I. Al Barazanchi, “Improving face recognition by elman neural network using curvelet transform and HSI color space,” Period. Eng. Nat. Sci., vol. 7, no. 2, pp. 430–437, 2019.

S. S. Oleiwi, G. N. Mohammed, and I. Al-barazanchi, “Mitigation of packet loss with end-to-end delay in wireless body area network applications,” Int. J. Electr. Comput. Eng., vol. 12, no. 1, pp. 460–470, 2022, doi: 10.11591/ijece.v12i1.pp460-470.




DOI: http://dx.doi.org/10.21533/pen.v10i2.2866

Refbacks

  • There are currently no refbacks.


Copyright (c) 2022 Sura Abdulmunem Mohammed Al-Juboori, Hissah Almutairi, Rasha Almajed, Amer Ibrahim, Hassan Muwafaq Gheni

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

ISSN: 2303-4521

Digital Object Identifier DOI: 10.21533/pen

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License