Abstract
Explainable AI (XAI) are the tools and frameworks of artificial intelligence applications that make it easier to trust the results and outcomes produced by machine learning algorithms. Additionally, XAI helps with debugging, enhancing model performance, and describing the behavior of models to others. This paper presents an innovative approach for hand-gesture detection using an Explainable AI Convolutional Neural Network (XAI-CNN) and SHAP (Shapley Additive Explanations) values, LIME (Local Interpretable Model-agnostic Explanations), and Anchor as Explainable AI tools. The XAI-CNN model is specifically designed for ten different classes of hand-gesture accurate recognition, including palm moved, C, ok, I, fist, index, palm, thumb, down, and fist moved symbols. The proposed XAI-CNN architecture, built upon the previous CNN model, demonstrates an impressive accuracy of 99.98%. Furthermore, the SHAP (XAI tools) values, LIME, and Anchor integration enable the interpretation and visualization of the model's decision-making process separately, enhancing the transparency and trustworthiness in the hand-gesture recognition process. This research contributes to the robustness and interpretable AI systems for hand-gesture recognition, empowering users with accurate and understandable AI technology.
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
Sheu, M.H., Morsalin, S.M.S., Hsu, C.C., Lai, S.C., Wang, S.H., Chang, C.Y.: Improvement of human pose estimation and processing with the intensive feature consistency network. IEEE Access 11, 28045–28059 (2023)
Flores, C.J.L., Cutipa, A.E.G., Enciso, R.L.: Application of convolutional neural networks for static hand gestures recognition under different invariant features. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, pp. 1–4 (2017)
Ribeiro, M.T., Singh, S., Guestrin C.: “Why Should I Trust You?”: explaining the predictions of any classifier. In: 2016 Conference of the North American Chapter of the Association for Computational Linguistics, San Diego, pp. 97–101 (2016)
Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: The Thirty-Second AAAI Conference (AAAI-2018), pp. 1527–1535 (2018)
Brito, L.C., Susto, G.A., Brito, J.N., Duarte, M.A.: An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. In: 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), Cusco, pp. 1–4 (2017)
Gozzi, N., Malandri, L., Mercorio, F., Pedrocchi, A.: An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. Mech. Syst. Signal Process. 163, 108105 (2022)
Bhandari, M., Yogarajah, P., Kavitha, M.S., Condell, J.: Exploring the capabilities of a lightweight CNN model in accurately identifying renal abnormalities: cysts, stones, and tumors, using LIME and SHAP. Appl. Sci. 13(5), 3125 (2023)
Aldughayfiq, B., Ashfaq, F., Jhanjhi, N., Humayun, M.: Explainable AI for retinoblastoma diagnosis: interpreting deep learning models with LIME and SHAP. Diagnostics 13(11), 1932 (2023)
Mahmoud, A.G., Hasan, A.M., Hassan, N.M.: Convolutional neural networks framework for human hand gesture recognition. Bull. Electr. Eng. Inf. 10(4), 2223–2230 (2021)
Meas, M., et al.: Explainability and transparency of classifiers for air-handling unit faults using explainable artificial intelligence (XAI). Sensors 22(17), 6338 (2022)
Sheu, M.H., Morsalin, S.M.S., Wang, S.H., Shen, Y.T., Hsia, S.C., Chang, C.Y.: FIBS-unet: feature integration and block smoothing network for single image dehazing. IEEE Access 10, 71764–71776 (2022)
Alani, A.A., Cosma, G., Taherkhani, A., McGinnity, T.M.: Hand gesture recognition using an adapted convolutional neural network with data augmentation. In: 2018 4th International Conference on Information Management (ICIM), Oxford, pp. 5–12 (2018)
Zhu, W.Y., Wong, W.K., Morsalin, S., Wang, S.H., Sheu, M.H.: Software and hardware integration system design with fruit identification for smart electronic scale applications. In: 2021 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW), Penghu, pp. 1–2 (2021)
Sharma, H.K., Kumar, P., Ahlawat, P., Manchanda, Y.: Deep learning based accurate hand gesture recognition using enhanced CNN model. In: Second International Conference on Computing (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 Singapore Pte Ltd.
About this paper
Cite this paper
Hsu, CC., Morsalin, S.M.S., Reyad, M.F., Shakib, N. (2024). Artificial Intelligence Model Interpreting Tools: SHAP, LIME, and Anchor Implementation in CNN Model for Hand Gestures Recognition. In: Lee, CY., Lin, CL., Chang, HT. (eds) Technologies and Applications of Artificial Intelligence. TAAI 2023. Communications in Computer and Information Science, vol 2074. Springer, Singapore. https://doi.org/10.1007/978-981-97-1711-8_2
Download citation
DOI: https://doi.org/10.1007/978-981-97-1711-8_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-1710-1
Online ISBN: 978-981-97-1711-8
eBook Packages: Computer ScienceComputer Science (R0)