References
[1]. Caffe. (n.d). Deep learning framework by BAIR. Retrieved from https://caffe.berkeleyvision.org/
[2]. Cao, Z., Hidalgo, G., Simon, T., Wei, S. E., & Sheikh, Y. (2018). OpenPose: realtime multi-person 2D pose estimation using Part Affinity Fields. arXiv preprint arXiv:1812.08008.
[3]. Cao, Z., Simon, T., Wei, S. E., & Sheikh, Y. (2017). Realtime multi-person 2D pose estimation using part affinity fields. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7291- 7299).
[4]. Gupta, B., Shukla, P., & Mittal, A. (2016, January). K-nearest correlated neighbor classification for Indian Sign Language gesture recognition using feature fusion. In 2016 International Conference on Computer Communication and Informatics (ICCCI) (pp. 1-5). IEEE. doi: 10.1109/IC-CCI.2016.7479951
[5]. Huang, J., Zhou, W., Li, H., & Li, W. (2015, July). Sign language recognition using real-sense. In 2015 IEEE China Summit and International Conference on Signal and Information Processing (ChinaSIP) (pp. 166-170). IEEE. doi: 10.1109/ChinaSIP 2015.7230384
[6]. Kumar, S., & Kaurav, A. (2018, January). Hand gesture through geometric moments (HCI based). In 2018 2nd International Conference on Inventive Systems and Control (ICISC) (pp. 561-565). IEEE. doi: 10.1109/ICISC.2018.8398862.
[7]. OpenPose (2019). Real-time multi-person keypoint detection library for body, face, hands, and foot estimation. Retrieved from https://github.com/CMUPerceptual- Computing-Lab/openpose
[8]. Simon, T., Joo, H., Matthews, I., & Sheikh, Y. (2017). Hand keypoint detection in single images using multiview bootstrapping. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1145- 1153).
[9]. Singh, A. K., John, B. P., Subramanian, S. V., Kumar, A. S., & Nair, B. B. (2016, December). A low-cost wearable Indian sign language interpretation system. In 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA) (pp. 1-6). IEEE. doi: 10.1109/RAHA.2016.7931873.
[10]. Vishwakarma, D. K., & Ansari, S. (2017, November). A framework for human-computer interaction using dynamic time warping and neural network. In 2017 International Conference on Inventive Computing and Informatics (ICICI) (pp. 242-246). IEEE. doi : 10.1109/ICICI.2017.8365346.
[11]. Wei, S. E., Ramakrishna, V., Kanade, T., & Sheikh, Y. (2016). Convolutional pose machines. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4724-4732).
