References
- 1Acharya, D, et al. 2018. “Covariance pooling for facial expression recognition”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 367–374. DOI: 10.1109/CVPRW.2018.00077
- 2Frizzell, K, et al. 2018. “Modifiable Intuitive Robot Controller: Computer Vision-Based Controller for Various Robotic Designs”. In: SoutheastCon2018. IEEE, 1–7. DOI: 10.1109/SECON.2018.8479064
- 3Goodfellow, I, et al. 2016.
Deep learning . vol. 1. Cambridge: MIT press. - 4Handa, A, Agarwal, R,
Dr , and Kohli, N,Prof . Dec. 2018. “A comprehensive video dataset for Multi-Modal Recognition Systems”. DOI: 10.5281/zenodo.1492227 - 5Jackson, Z, et al. 2018. Jakobovski/free-spoken-digit-dataset v1. 0.7.
- 6Krizhevsky, A, Sutskever, I and Hinton, GE. 2012. “Imagenet classification with deep convolutional neural networks”. In: Advances in neural information processing systems, 1097–1105.
- 7Lai, Y. 2012. Human-machine interaction system. US Patent App. 13/086,394.
- 8Lyons, M, et al. 1998. “Coding facial expressions with gabor wavelets”. In: Proceedings Third IEEE international conference on automatic face and gesture recognition. IEEE, 200–205. DOI: 10.1109/AFGR.1998.670949
- 9Memos, VA. et al. 2018. “An efficient algorithm for media-based surveil-lance system (EAMSuS) in IoT smart city framework”. In: Future Generation Computer Systems, 83: 619–628. DOI: 10.1016/j.future.2017.04.039
- 10Other. 2019. NeatVideo.
https://www.neatvideo.com/ . - 11Shekar Naganna, S, Seth, A, Tomar, V and Yellareddy, SR. 2018. Face recognition in big data ecosystem using multiple recognition models. U.S. Patent Application 15/957,884.
- 12Simonyan, K and Zisserman, A. 2014. “Very deep convolutional net-works for large-scale image recognition”. In: arXiv preprint arXiv, 1409.1556
- 13Sun, X, Wu, P and Hoi, SCH. 2018. “Face detection using deep learning: An improved faster RCNN approach”. In: Neurocomputing, 299: 42–50. DOI: 10.1016/j.neucom.2018.03.030
- 14Zhao, B, et al. 2017. “Waveforms classification based on convolutional neural networks”. In: 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 162–165. DOI: 10.1109/IAEAC.2017.8053998