International Journal of Innovative Research in Computer and Communication Engineering

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TITLE Hand Gesture Control Presentation System: A Computer Vision Framework for Touchless Slide Navigation
ABSTRACT Conventional presentation systems depend on physical input devices such as keyboards, mice, or remote clickers, which can restrict a presenter's mobility and reduce natural interaction with an audience [1][2]. Hand gesture recognition offers a touchless and intuitive alternative by allowing presenters to control slides directly through hand movements captured by a standard webcam. This paper presents a Hand Gesture Control Presentation System that applies computer vision and machine learning techniques to detect, track, and classify hand gestures in real time. The system uses a hand-landmark detection pipeline [3][4] to extract twenty-one key points per hand, from which geometric features such as finger angles and inter-joint distances are computed. These features are passed to a trained classifier that recognizes gestures including swipe right, swipe left, pointer mode, and session-end, which are mapped to corresponding presentation commands such as next slide, previous slide, cursor control, and session termination. The proposed framework removes the dependency on physical remotes, improves accessibility, and provides a smooth, interactive presentation experience. Experimental evaluation shows that the system achieves reliable real-time gesture recognition with low latency under normal lighting conditions.
AUTHOR DR. T.V.S SRIRAM, D. APPALARAJU, S. TIRUPATHI RAO Sr. Asst. Professor, Dept. of MCA, NSRIT, Visakhapatnam, AP, India Asst. Professor, Dept. Of MCA, NSRIT, Visakhapatnam, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India
VOLUME 185
DOI DOI: 10.15680/IJIRCCE.2026.1406097
PDF pdf/97_Hand Gesture Control Presentation System A Computer Vision Framework for Touchless Slide Navigation.pdf
KEYWORDS
References [1] S. Mitra and T. Acharya, "Gesture Recognition: A Survey," IEEE Transactions on Systems, Man, and Cybernetics, Part C, vol. 37, no. 3, pp. 311-324, 2007.
[2] R. S. Pressman and B. R. Maxim, Software Engineering: A Practitioner's Approach, 8th ed., McGraw-Hill Education, 2015.
[3] C. Lugaresi et al., "MediaPipe: A Framework for Building Perception Pipelines," arXiv preprint arXiv:1906.08172, 2019.
[4] F. Zhang et al., "MediaPipe Hands: On-device Real-time Hand Tracking," arXiv preprint arXiv:2006.10214, 2020.
[5] M. Turk, "Gesture Recognition," in Handbook of Virtual Environment Technology, Lawrence Erlbaum Associates, 2002.
[6] S. Rautaray and A. Agrawal, "Vision Based Hand Gesture Recognition for Human Computer Interaction: A Survey," Artificial Intelligence Review, vol. 43, no. 1, pp. 1-54, 2015.
[7] Google AI, "Hand Landmarks Detection Guide," MediaPipe Solutions Documentation, 2023.
[8] C. Cortes and V. Vapnik, "Support Vector Networks," Machine Learning, vol. 20, pp. 273-297, 1995.
[9] L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
[10] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
[11] B. K. Chakraborty, D. Sarma, M. K. Bhuyan, and K. F. MacDorman, "Review of Constraints on Vision-Based Gesture Recognition for Human-Computer Interaction," IET Computer Vision, vol. 12, no. 1, pp. 3-15, 2018.
[12] M. Bhuiyan and R. Picking, "A Gesture Controlled User Interface for Inclusive Design and Evaluative Study of Its Usability," Journal of Software Engineering and Applications, vol. 4, no. 9, pp. 513-521, 2011.
[13] J. L. Hernandez-Rebollar, R. W. Lindeman, and N. Kyriakopoulos, "A Multi-Class Pattern Recognition System for Practical Finger Spelling Translation," Proc. IEEE ICMI, pp. 185-190, 2002.
[14] T. Mitchell, Machine Learning, McGraw-Hill, 1997.
[15] A. Krizhevsky, I. Sutskever, and G. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," NIPS, 2012.
[16] H. Khanum and H. B. Pramod, "Smart Presentation Control by Hand Gestures Using Computer Vision and Google's MediaPipe," International Research Journal of Engineering and Technology, vol. 9, no. 7, 2022.
[17] M. Paulson, N. P. R., S. Davis, and S. Varma, "Smart Presentation Using Gesture Recognition," International Journal for Research Trends and Innovation, vol. 2, no. 3, 2017.
[18] D. R. Jadhav and L. M. R. J. Lobo, "Navigation of PowerPoint Using Hand Gestures," International Journal of Science and Research, vol. 4, no. 1, 2015.
[19] S. Bhisikar et al., "Hand Gesture Recognition for Human-Computer Interaction," International Journal of Computer Applications, vol. 158, no. 3, 2017.
[20] G. Bradski, "The OpenCV Library," Dr. Dobb's Journal of Software Tools, 2000.
[21] G. Bradski and A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library, O'Reilly Media, 2008.
[22] F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
[23] C. R. Harris et al., "Array Programming with NumPy," Nature, vol. 585, pp. 357-362, 2020.
[24] S. Hochreiter and J. Schmidhuber, "Long Short-Term Memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
[25] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2016.
[26] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
[27] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, 2017.
[28] R. Y. Wang and J. Popovic, "Real-Time Hand-Tracking with a Color Glove," ACM Transactions on Graphics, vol. 28, no. 3, 2009.
[29] J. Suarez and R. R. Murphy, "Hand Gesture Recognition with Depth Images: A Review," Proc. IEEE RO-MAN, pp. 411-417, 2012.
[30] Z. Ren, J. Yuan, J. Meng, and Z. Zhang, "Robust Part-Based Hand Gesture Recognition Using Kinect Sensor," IEEE Transactions on Multimedia, vol. 15, no. 5, pp. 1110-1120, 2013.
[31] A. Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 3rd ed., O'Reilly Media, 2022.
[32] S. Raschka and V. Mirjalili, Python Machine Learning, 3rd ed., Packt Publishing, 2019.
[33] W. McKinney, Python for Data Analysis, 3rd ed., O'Reilly Media, 2022.
[34] I. Sommerville, Software Engineering, 10th ed., Pearson Education, 2016.
[35] M. Z. Uddin, C. Boletsis, and P. Rudshavn, "Real-Time Norwegian Sign Language Recognition Using MediaPipe and LSTM," Multimodal Technologies and Interaction, vol. 9, no. 3, pp. 1-15, 2025.
[36] E. Fertl, E. Castillo, G. Stettinger, M. P. Cuéllar, and D. P. Morales, "Hand Gesture Recognition on Edge Devices: Sensor Technologies, Algorithms, and Processing Hardware," Sensors, vol. 25, no. 6, pp. 1-46, 2025.
[37] V. Bhat and B. S. Panchami, "Hand Gesture-Based Mouse Control System Using OpenCV and MediaPipe for Real-Time Interaction," IRE Journals, vol. 8, no. 6, pp. 862-867, 2024.
[38] K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
[39] S. Haykin, Neural Networks and Learning Machines, 3rd ed., Pearson, 2009.
[40] A. Esteva et al., "A Guide to Deep Learning in Healthcare," Nature Medicine, vol. 25, no. 1, pp. 24-29, 2019.
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