Application of Deep Learning using Convolutional Neural Network (CNN) Algorithm for Gesture Recognition

Ahmad Abuzar Alhamdani

Abstract


Gesture recognition is a fascinating method of human-computer interaction that goes beyond traditional means such as keyboards, pointers, and joypads. In gesture recognition, Convolutional Neural Network (CNN) algorithms are utilized in Deep Learning to train models using datasets comprising gesture images. The training process involves pattern recognition and identification of crucial features from gesture images, followed by evaluation to measure the model's accuracy. Gesture recognition holds immense potential across various fields, including human-computer interaction, gaming, healthcare, and autonomous vehicles, and continues to be a focus of research and development in the future.


Keywords


CNN; Deep learning; Gesture recognition

Full Text:

PDF

References


Aamir, M., Rahman, Z., Abro, W. A., Tahir, M., & Ahmed, S. M. (2019). An optimized architecture of image classification using convolutional neural network. International Journal of Image, Graphics and Signal Processing, 10(10), 30-39.

Abdulhai, B., Pringle, R., and Karakoulas, G. J. (2003). Reinforcement learning for true adaptive traffic signal control. Journal of Transportation Engineering, 129(3), 278-285.

Al-Doori, S. K. S., Taspinar, Y. S., & Koklu, M. (2021). Distracted driving detection with machine learning methods by CNN based feature extraction. International Journal of Applied Mathematics Electronics and Computers, 9(4), 116-121.

Ando, R. K., Zhang, T., and Bartlett, P. (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research, 6(11), 1817-1853.

Arifin, I., Haidi, R. F., and Dzalhaqi, M. (2021). Penerapan computer vision menggunakan metode deep learning pada perspektif generasi ulul albab. Jurnal Teknologi Terpadu, 7(2), 98-107.

Barbhuiya, A. A., Karsh, R. K., and Jain, R. (2021). CNN based feature extraction and classification for sign language. Multimedia Tools and Applications, 80(2), 3051-3069.

Choldun, M. I., and Surendro, K. (2018). Klasifikasi penelitian dalam deep learning. Improve, 10(1), 25-33.

Dasgupta, A., & Nath, A. (2016). Classification of machine learning algorithms. International Journal of Innovative Research in Advanced Engineering (IJIRAE), 3(3), 6-11.

Hu, F., Xia, G. S., Hu, J., and Zhang, L. (2015). Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11), 14680-14707.

Ju, C., Bibaut, A., and van der Laan, M. (2018). The relative performance of ensemble methods with deep convolutional neural networks for image classification. Journal of Applied Statistics, 45(15), 2800-2818.

Kaliyar, R. K., Goswami, A., and Narang, P. (2021). Fakebert: Fake news detection in social media with a bert-based deep learning approach. Multimedia tools and applications, 80(8), 11765-11788.

Khan, R. Z., and Ibraheem, N. A. (2012). Hand gesture recognition: A literature review. International journal of artificial Intelligence & Applications, 3(4), 161-174.

Kurniawan, A. A., and Mustikasari, M. (2021). Implementasi deep learning menggunakan metode CNN dan lSTM untuk menentukan berita palsu dalam bahasa indonesia. Jurnal Informatika Universitas Pamulang, 5(4), 544-552.

Ridwang, R. (2017). Pengenalan Bahasa Isyarat Indonesia (SIBI) menggunakan leap motion controller dan algoritma data mining naïve bayes. Jurnal INSYPRO (Information System and Processing), 2(2), 1-8.

Rijanandi, T., Rizaldy, A. A., Kridabayu, A. N., Devara, E. G. E., and Adhinata, F. D. (2022). Penerapan hair recognition menggunakan metode haar cascade classifier dan CNN deep learning. Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar, 8(1), 53-57.

Roihan, A., Sunarya, P. A., and Rafika, A. S. (2020). Pemanfaatan machine learning dalam berbagai bidang. Indonesian Journal on Computer and Information Technology, 5(1), 75-82.

Setiawan, D. (2018). Dampak perkembangan teknologi informasi dan komunikasi terhadap budaya. JURNAL SIMBOLIKA: Research and Learning in Communication Study (E-Journal), 4(1), 62-72.

Mesut Toğaçar, Burhan Ergen, and Zafer Cömert. (2019). BrainMRNet: Brain tumor detection using magnetic resonance images with a novel convolutional neural network model. Medical Hypotheses, 134, 1-23.

Wahyuni, S., and Sulaeman, M. (2022). Penerapan algoritma deep learning untuk sistem absensi kehadiran deteksi wajah di PT Karya Komponen Presisi. Jurnal Informatika SIMANTIK, 7(1), 12-21.

Yan, J., and Wang, X. (2022). Unsupervised and semi‐supervised learning: The next frontier in machine learning for plant systems biology. The Plant Journal, 111(6), 1527-1538.




DOI: https://doi.org/10.17509/seict.v2i1.34673

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Journal of Software Engineering, Information and Communication Technology (SEICT)

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

Journal of Software Engineering, Information and Communicaton Technology (SEICT), 
(e-ISSN:
2774-1699 | p-ISSN:2744-1656) published by Program Studi Rekayasa Perangkat Lunak, Kampus UPI di Cibiru.


 Indexed by.