Adapting Viola-Jones Method for Online Hand / Glove Identification


  • Taib Shamsadin Abdulsamad Department of Computer, College of Basic Education, University of Raparin, Sulaymaniyah, Iraq; Department of Computer Science, College of Science and Technology, University of Human Development, Sulaymaniyah, Iraq
  • Mahmud Abdulla Mohammad Department of Computer, College of Basic Education, University of Raparin, Sulaymaniyah, Iraq; Department of Information Technology, College of Science and Technology, University of Human Development, Sulaymaniyah, Iraq
  • Faraidoon Hassan Ahmad Department of Pharmacognosy & Pharmaceutical Chemistry, College of Pharmacy, University of Sulaimani, Sulaymaniyah, Iraq; Department of Information Technology, University College of Goizha, Sulaymaniyah, Iraq



Computer Vision, Image Processing, Object Detection, Viola-Jones, Hand Detection, Identification.


This article proposes a method for hand identification, adapting the method of Viola-Jones for identifying two different objects. The main objective of this work is to solve the problems of hand identification. Thus, our approach based on learning for two objects as one package. Also, the proposed method folds into three parts; the first part is training for both objects, second detection of both objects, and third the identification step to identify if the hand is wearing a glove or not, then labeling each one with a suitable state. Moreover, to test our method, we have proposed a new dataset, which includes a variety of cases with different compositions of hand. As a result, 8 cases were used to test the method. The method was able to detect a human hand successfully. Additionally, it could identify whether the hand was or was not wearhing a glove.  The accuracy of detecting a hand without a glove was about 63%, and the accuracy of detecting a hand with a glove on was about 61%. Even though the tests scored different accuracy, as a first step towards solving this problem, it is a big achievement to even reach this level of accuracy.


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Acharya, T., & Ray, A. K. (2005). Image Processing: Principles and Applications. USA: John Wiley and Sons. pp. 1–426. doi:
Ahmad, F. H. (2015). Efficient Facial Image Feature Extraction Method for Ethnicity Identification, M.Sc. Thesis. College of Commerce, University of Sulaimani, Sulaimani. pp. 1–71
Ahmed, A., Salam, B., Mohammad, M., Akgul, A., & Khoshnaw, S. H. A. (2020). Analysis coronavirus disease (COVID-19) model using numerical approaches and logistic model. AIMS Bioeng., 7(3), 130–146.
Cevallos, C., Ponce, H., Moya-Albor, E., & Brieva, J. (2020, July 1). Vision-Based Analysis on Leaves of Tomato Crops for Classifying Nutrient Deficiency using Convolutional Neural Networks. Proceedings of the International Joint Conference on Neural Networks, pp. 1-7. doi:
Chouvatut, V., Yotsombat, C., Sriwichai, R., & Jindaluang, W. (2015). Multi-view hand detection applying viola-jones framework using SAMME AdaBoost. Proceedings of the 2015-7th International Conference on Knowledge and Smart Technology, KST 2015. pp. 30-35. doi:
Da’San, M., Alqudah, A., & Debeir, O. (2015). Face detection using Viola and Jones method and neural networks. 2015 International Conference on Information and Communication Technology Research, ICTRC 2015. pp. 40-43, doi :
Dey, S., Howlader, A., & Deb, C. (2021). MobileNet Mask: A Multi-phase Face Mask Detection Model to Prevent Person-To-Person Transmission of SARS-CoV-2, pp. 603–613. doi:
Hazim, N., Sameer, S., Esam, W., & Abdul, M. (2016). Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance. International Journal of Advanced Computer Science and Applications (IJACSA), 7(5) 371-377. doi:
Hendra, T., Spolaor, R., & Chen, Z. (2019). A Compound Technique for Multiple Objects Detection Based on Markov Clustering Networks and Viola-Jones Algorithm. 2019 IEEE 2nd International Conference on Information Communication and Signal Processing (ICICSP), pp. 459–463. doi:
Kolsch, M., & Turk, M. (2004). Robust hand detection. Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings., FGR Vol. 4, pp. 614-619. doi:
Kovalenko, M., Antoshchuk, S., & Sieck, J. (2014). Real-time hand tracking and gesture recognition using semantic-probabilistic network. Proceedings - UKSim-AMSS 16th International Conference on Computer Modelling and Simulation, UKSim 2014, pp. 269-274. doi:
Mao, G. Z., Wu, Y. L., Hor, M. K., & Tang, C. Y. (2009). Real-time hand detection and tracking against complex background. IIH-MSP 2009 - 2009 5th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 905–908. doi:
Mohammad, M., Hicks, Y., & Kaloskampis, I. (2016). Video-based Road Detection Using Evolving GMMs and Region Enhancement. 11th International IMA Conference on Mathematics in Signal Processing, Birmingham, Dec 2016.
Muhammad, M. A. (2016). Video-based Situation Assessment for Road Safety. Ph.D. Thesis, Cardiff University, Cardiff, 1-187.
Murthy, C. B., Hashmi, M. F., Bokde, N. D., & Geem, Z. W. (2020). Investigations of object detection in images/videos using various deep learning techniques and embedded platforms-A comprehensive review. Applied Sciences (Switzerland), 10(9). doi:
Nguyen, V.-T., Le, T., Tran, T.-H., Mullot, R., & Courboulay, V. (2012). A method for hand detection based on Internal Haar-like features and Cascaded AdaBoost Classifier. Conference: Proceedings of The Fourth International Conference on Communications and Electronics (ICCE 2012), pp. 608–613.
Verschae, R., & Ruiz-del-Solar, J. (2015). Object detection: Current and future directions. Frontiers Robotics AI, 2(NOV). doi:
Viola, P., & Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1. CVPR 2001, pp. 1-9. doi:
Wang, Z., Wang, G., Huang, B., Xiong, Z., Hong, Q., Wu, H., Yi, P., Jiang, K., Wang, N., Pei, Y., Chen, H., Yu, M., Huang, Z., & Liang, J. (2020). Masked Face Recognition Dataset and Application. arXiv preprint arXiv:2003.09093.
Yun, L., & Peng, Z. (2009). An automatic hand gesture recognition system based on Viola-Jones method and SVMs. 2nd International Workshop on Computer Science and Engineering, WCSE 2009, 2, pp. 72–76. doi:






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How to Cite

Adapting Viola-Jones Method for Online Hand / Glove Identification. (2021). UKH Journal of Science and Engineering, 5(1), 80-90.

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