Gesture Recognition using Conditional Generative Adversarial Networks


  • Gladys Putri Universitas Pradita
  • Handri Santoso Universitas Pradita




Sign language, Generative Adversarial Networks, Deep Learning, Convolutional Neural Network, Computer Vision


Sign language is very useful for giving signs to communication partners, in this case, signs are not only for people with disabilities but can also be used by normal people. Even in children's or adult games, sign language is used as a language or a means of communication with one another. Recognition of sign language using a computer by doing several methods, because the computer does not recognize the image of a sign with a certain meaning. Therefore, it is necessary to train computers to recognize these signs. One of the fields that discusses gesture image recognition is the field of Computer Vision. Where the science of computer vision is able to process the image. In addition, in image processing, it is necessary to carry out deep learning processes such as Convolutional Neural Networks. In the Convolutional Neural Network algorithm, there are also many methods or architectures such as VGG16, VGG19, ResNet-50, DenseNet, Inception_V3, and many more. The use of architecture is used in accordance with existing needs. Therefore, the choice of architecture will determine the model is built or not to build from scratch, only transfer learning or pre-train. Pre-training is done by using the initial model and then using it only. Or do some training. The purpose of this study was to detect sign language using the Generative Adversarial Network (GAN). Actually, the Generative Adversarial Network method is widely used in making synthetic images, but this time the Generative Adversarial Network can also detect images from sign language.

GS Cited Analysis


Download data is not yet available.


Awan, S. E., Bennamoun, M., Sohel, F., Sanfilippo, F., & Dwivedi, G. (2021). Imputation of missing data with class imbalance using conditional generative adversarial networks. Neurocomputing, 453, 164–171.

Bachute, M. R., & Subhedar, J. M. (2021). Autonomous Driving Architectures: Insights of Machine Learning and Deep Learning Algorithms. Machine Learning with Applications, 6(March), 100164.

Damatraseta, F., Novariany, R., & Ridhani, M. A. (2021). Real-time BISINDO Hand Gesture Detection and Recognition with Deep Learning CNN. Jurnal Informatika Kesatuan, 1(1), 71–80.

Fadillah, R. Z., Irawan, A., Susanty, M., & Artikel, I. (2021). Data Augmentasi Untuk Mengatasi Keterbatasan Data Pada Model Penerjemah Bahasa Isyarat Indonesia (BISINDO). Jurnal Informatika, 8(2), 208–214.

Goodfellow, I. J., Mirza, M., Courville, A., & Bengio, Y. (2013). Multi-prediction deep Boltzmann machines. Advances in Neural Information Processing Systems, 1–9.

Goodfellow, I., Mehdi, M., Bing, X., & David, W. (2014). Generative Adversarial Nets.

Gruschwitz, P., Grunz, J.-P., Kuhl, P. J., Kosmala, A., Bley, T. A., Petritsch, B., & Heidenreich, J. F. (2021). Performance testing of a novel deep learning algorithm for the detection of intracranial hemorrhage and first trial under clinical conditions. Neuroscience Informatics, 1(1–2), 100005.

Isola, P., Jun-Yan, Z., Tinghui, Z., & Alexei, E. (2019). Image-to-Image Translation with Conditional Adversarial Networks. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11129 LNCS, 601–618.

Liu, J., Zhu, G., & Yin, J. (2021). Joint color spectrum and conditional generative adversarial network processing for underwater acoustic source ranging. Applied Acoustics, 182, 108244.

Mirza, M., & Osindero, S. (2014). Conditional Generative Adversarial Nets. 1–7.

Mishra, P., & Herrmann, I. (2021). GAN meets chemometrics: Segmenting spectral images with pixel2pixel image translation with conditional generative adversarial networks. Chemometrics and Intelligent Laboratory Systems, 215(April), 104362.

Sholawati, M., Auliasari, K., & Ariwibisono, F. X. (2018). Pengembangan Aplikasi Pengenalan Bahasa Isyarat Abjad Sibi Menggunakan Metode Convolutional Neural Network ( Cnn ).

Sindarto, S. S., Ratnawati, D. E., & Arwani, I. (2022). Klasifikasi Citra Sistem Isyarat Bahasa Indonesia ( SIBI ) dengan Metode Convolutional Neural Network pada Perangkat Lunak berbasis Android. 6(5), 2129–2138.

Susanty, M., Fadillah, R. Z., & Irawan, A. (2021). Model Penerjemah Bahasa Isyarat Indonesia (BISINDO) Menggunakan Pendekatan Transfer Learning. Petir, 15(1), 1–9.

Xia, Y., Ravikumar, N., Greenwood, J. P., Neubauer, S., Petersen, S. E., & Frangi, A. F. (2021). Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning. Medical Image Analysis, 71, 102037.

Yu, L., Xue, L., Liu, F., Li, Y., Jing, R., & Luo, J. (2022). The applications of deep learning algorithms on in silico druggable proteins identification. Journal of Advanced Research, xxxx.


Crossmark Updates

How to Cite

Putri, G., & Santoso, H. . (2022). Gesture Recognition using Conditional Generative Adversarial Networks. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 7(2), 532-540.

Most read articles by the same author(s)

1 2 > >>