Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language

Authors

  • Ramesh M. Badiger Department of Computer Science and Engineering, Tontadarya College of Engineering, India
  • Rajesh Yakkundimath Department of Computer Science and Engineering, KLE Institute of Technology, India
  • Guruprasad Konnurmath School of Computer Science and Engineering, KLE Technological University, Hubballi, India
  • Praveen M. Dhulavvagol School of Computer Science and Engineering, KLE Technological University, Hubballi, India
Volume: 14 | Issue: 2 | Pages: 13255-13260 | April 2024 | https://doi.org/10.48084/etasr.6864

Abstract

This study focuses on recognizing and categorizing South Indian Sign Language gestures based on different age groups through transfer learning models. Sign language serves as a natural and expressive communication method for individuals with hearing impairments. This study intends to develop deep transfer learning models, namely Inception-V3, VGG-16, and ResNet-50, to accurately identify and classify double-handed gestures in South Indian languages, like Kannada, Tamil, and Telugu. A dataset comprising 30,000 images of double-handed gestures, with 10,000 images for each considered age group (1-7, 8-25, and 25 and above), is utilized to enhance and modify the models for improved classification performance. Amongst the tested models, Inception-V3 achieves the best performance with a test precision of 95.20% and validation accuracy of 92.45%, demonstrating its effectiveness in accurately categorizing images of double-handed gestures into ten different classes.

Keywords:

sign language, age group, gesture identification, transfer learning, Inception-V3, VGG-16, ResNet-50

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References

H. G. Doan and N. T. Nguyen, "Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition," Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8628–8633, Jun. 2022.

M. A. Abderrahmane, I. Guelzim, and A. A. Abdelouahad, "Human Age Prediction Based on Hand Image using Multiclass Classification," in 2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), Sakheer, Bahrain, Jul. 2020.

E. Aydemir and R. Alalawi, "Classification Of Hand Images by Person, Age and Gender with The Median Robust Extended Local Binary Model," Balkan Journal of Electrical and Computer Engineering, vol. 11, no. 1, pp. 78–87, Jan. 2023.

G. Bakshi, A. Aggarwal, D. Sahu, R. R. Baranwal, G. Dhall, and M. Kapoor, "Age, Gender, and Gesture Classification Using Open-Source Computer Vision," in Emerging Technologies in Data Mining and Information Security, Singapore, 2023, pp. 63–73.

S. K. Gupta and N. Nain, "Review: Single attribute and multi attribute facial gender and age estimation," Multimedia Tools and Applications, vol. 82, no. 1, pp. 1289–1311, Jan. 2023.

W.-B. Horng, C.-P. Lee, and C.-W. Chen, "Classification of Age Groups Based on Facial Features," Tamkang Journal of Science and Engineering, vol. 4, no. 3, pp. 183–192, Sep. 2001.

K. R, "Deep Learning for Age Group Classification System," International Journal of Advances in Signal and Image Sciences, vol. 4, no. 2, pp. 16–22, Dec. 2018.

M. Madhiarasan and P. P. Roy, "A Comprehensive Review of Sign Language Recognition: Different Types, Modalities, and Datasets." arXiv, Apr. 7, 2022.

N. S. Russel and A. Selvaraj, "Gender discrimination, age group classification and carried object recognition from gait energy image using fusion of parallel convolutional neural network," IET Image Processing, vol. 15, no. 1, pp. 239–251, 2021.

R. M. Badiger and D. Lamani, "Deep Learning Based South Indian Sign Language Recognition by Stacked Autoencoder Model and Ensemble Classifier on Still Images and Videos," Journal of Theoretical and Applied Information Technology, vol. 100, no. 21, pp. 6587–6597, 2022.

N. C. Kundur, B. C. Anil, P. M. Dhulavvagol, R. Ganiger, and B. Ramadoss, "Pneumonia Detection in Chest X-Rays using Transfer Learning and TPUs," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11878–11883, Oct. 2023.

N. Kumar, A. Hashmi, M. Gupta, and A. Kundu, "Automatic Diagnosis of Covid-19 Related Pneumonia from CXR and CT-Scan Images," Engineering, Technology & Applied Science Research, vol. 12, no. 1, pp. 7993–7997, Feb. 2022.

K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 770–778.

S. Khan, I. Ali, F. Ghaffar, and Q. Mazhar-ul-Haq, "Classification of Macromolecules Based on Amino Acid Sequences Using Deep Learning," Engineering, Technology & Applied Science Research, vol. 12, no. 6, pp. 9491–9495, Dec. 2022.

G. Konnurmath and S. Chickerur, "An Investigation into Power Aware Aspects of Rendering 3D Models on Multi-Core Processors," Procedia Computer Science, vol. 218, pp. 887–898, Jan. 2023.

G. Konnurmath and S. Chickerur, "GPU Shader Analysis and Power Optimization Model," Engineering, Technology & Applied Science Research, vol. 14, no. 1, pp. 12925–12930, 2024.

G. Konnurmath and S. Chickerur, "Power-Aware Characteristics of Matrix Operations on Multicores," Applied Artificial Intelligence, vol. 35, no. 15, pp. 2102–2123, Dec. 2021.

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How to Cite

[1]
Badiger, R.M., Yakkundimath, R., Konnurmath, G. and Dhulavvagol, P.M. 2024. Deep Learning Approaches for Age-based Gesture Classification in South Indian Sign Language. Engineering, Technology & Applied Science Research. 14, 2 (Apr. 2024), 13255–13260. DOI:https://doi.org/10.48084/etasr.6864.

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