Open Access
ITM Web Conf.
Volume 44, 2022
International Conference on Automation, Computing and Communication 2022 (ICACC-2022)
Article Number 03058
Number of page(s) 5
Section Computing
Published online 05 May 2022
  1. Matsugu, Masakazu, et al. “Subject independent facial expression recognition with robust face detection using a convolutional neural network.” Neural Networks 16.5-6 (2003): 555–559. [CrossRef] [Google Scholar]
  2. Zhang, Ligang, and Dian Tjondronegoro. “Facial expression recognition using facial movement features.” IEEE transactions on affective computing 2.4 (2011): 219–229. [CrossRef] [Google Scholar]
  3. Hayat, Munawar, and Mohammed Bennamoun. “An automatic framework for textured 3D videobased facial expression recognition.” IEEE Transactions on Affective Computing 5.3 (2014): 301–313. [CrossRef] [Google Scholar]
  4. Hablani, Ramchand, Narendra Chaudhari, and Sanjay Tanwani. “Recognition of facial expressions using local binary patterns of important facial parts.” International Journal of Image Processing (IJIP) 7.2 (2013): 163–170. [Google Scholar]
  5. Li, Zisheng, Junichi Imai, and Masahide Kaneko. “Facial-component-based bag of words and phog descriptor for facial expression recognition.” 2009 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2009. [Google Scholar]
  6. Yu, Zhiding, and Cha Zhang. “Image based static facial expression recognition with multiple deep network learning.” Proceedings of the 2015 ACM on international conference on multimodal interaction. 2015. [Google Scholar]
  7. Sekaran, Sarmela A.P. Raja, Chin Poo Lee, and Kian Ming Lim. “Facial emotion recognition using transfer learning of AlexNet.” 2021 9th International Conference on Information and Communication Technology (ICoICT). IEEE, 2021. [Google Scholar]
  8. Hussain, Shaik Asif, and Ahlam Salim Abdallah Al Balushi. “A real time face emotion classification and recognition using deep learning model.” Journal of Physics: Conference Series. Vol. 1432. No. 1. IOP Publishing, 2020. [Google Scholar]
  9. Verma, Monu, Santosh Kumar Vipparthi, Girdhari Singh, and Subrahmanyam Murala. “LEARNet: Dynamic imaging network for micro expression recognition.” IEEE Transactions on Image Processing 29 (2019): 1618–1627. [Google Scholar]
  10. [Google Scholar]

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