Abstract
The primary objective of a neural network is to achieve generalization, enabling it to recognize previously unseen data from the same category. This concept facilitates the transfer of knowledge between neural networks trained for different purposes. In this study, we investigate the utilization of deep Autoencoder neural networks for unsupervised learning as a regression network to improve the performance of a categorization network. We propose a novel deep Autoencoder network designed to accomplish this goal. Our proposed architecture is applied to video data, specifically focusing on hand gesture recognition using the Chalearn 2014 and IsoGD datasets. Hand gesture recognition plays a crucial role in human-machine interactions, despite challenges posed by temporal information, gesture overlap in videos, and variations in hand gesture orientation. Through our research, we have successfully enhanced the recognition accuracy from 46.1% to 76.4% by employing transfer learning techniques within the same dataset and across different datasets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Erhan, D., Bengio, Y., Courville, A., Manzagol, P.-A., Vincent, P., Bengio, S.: Why does unsupervised pre-training help deep learning?. J. Mach. Learn. Res. 625–660 (2010)
Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using LSTMs. In: International Conference on Machine Learning, pp. 843–852 (2015)
Saber, A., Sakr, M., Abo-Seida, O.M., Keshk, A., Chen, H.: A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 9, 71194–71209 (2021). https://doi.org/10.1109/ACCESS.2021.3079204
Neupane, B., Horanont, T., Aryal, J.: Real-time vehicle classification and tracking using a transfer learning-improved deep learning network. Sensors 22(10), 3813 (2022). https://doi.org/10.3390/s22103813
Wu, Z., et al.: Accelerating heat exchanger design by combining physics-informed deep learning and transfer learning. Chem. Eng. Sci. 282, 119285 (2023)
Nabipour, M., Nayyeri, P., Jabani, H., Mosavi, A., Salwana, E.: Deep learning for stock market prediction. Entropy 22(8), 840 (2020)
Gao, W., Mahajan, S.P., Sulam, J., Gray, J.J.: Deep learning in protein structural modeling and design. Patterns 1(9) (2020)
Long, M., Cao, Y., Wang, J., Jordan, M.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning, pp. 97–105 (2015)
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning, pp. 2208–2217. PMLR (2017)
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)
Karpathy, A., et al.: Large scale video classification with convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1725–1732 (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017)
Lore, K.G., So, Akintayo, A., Sarkar, S.: LLNet: a deep autoencoder approach to natural low-light image enhancement. ScienceDirect 61, 650–662 (2017)
Alo, U.R., Nweke, H.F., Teh, Y.W., Murtaza, G.: Smartphone motion sensor-based complex human activity identification using deep stacked autoencoder algorithm for enhanced smart healthcare system. Sensors 20(21), 6300 (2020)
Ahmed, I., Ahmad, M., Chehri, A., Jeon, G.: A smart-anomaly-detection system for industrial machines based on feature autoencoder and deep learning. Micromachines 14(1), 154 (2023). https://doi.org/10.3390/mi14010154
Bui, T.T.T., Tran, X.N., Phan, A.H.: Deep learning based MIMO systems using open-loop autoencoder. AEU-Int. J. Electron. Commun. 168, 154712 (2023)
Wan, J., Zhao, Y., Zhou, S., Guyon, I., Escalera, S., Li, S.Z.: Chalearn looking at people RGB-D isolated and continuous datasets for gesture recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 56–64 (2016)
Escalera, S., et al.: ChaLearn looking at people challenge 2014: dataset and results. In: Agapito, L., Bronstein, M.M., Rother, C. (eds.) ECCV 2014. LNCS, vol. 8925, pp. 459–473. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16178-5_32
Parkhi, O., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC 2015- Proceedings of the British Machine Vision Conference 2015. British Machine Vision Association (2015)
Escobedo-Cardenas, E., Camara-Chavez, G.: A robust gesture recognition using hand local data and skeleton trajectory. In: IEEE International Conference on Image Processing (ICIP), pp. 1240–1244. IEEE (2015)
**, C., Koskela, M.: Using appearance-based hand features for dynamic RGB-D gesture recognition. In: 22nd International Conference on Pattern Recognition (ICPR), pp. 411–416. IEEE (2014)
Tur, A.O., Keles, H.Y.: Evaluation of hidden Markov models using deep CNN features in isolated sign recognition. Multimed. Tools Appl. 80 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mira, A. (2024). Exploring Enhanced Recognition in Gesture Language Videos Through Unsupervised Learning of Deep Autoencoder. In: Al-Bakry, A.M., et al. New Trends in Information and Communications Technology Applications. NTICT 2023. Communications in Computer and Information Science, vol 2096. Springer, Cham. https://doi.org/10.1007/978-3-031-62814-6_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-62814-6_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-62813-9
Online ISBN: 978-3-031-62814-6
eBook Packages: Computer ScienceComputer Science (R0)