Development of video-based emotion recognition using deep learning with Google Colab - Teddy Surya Gunawan; Arselan Ashraf; Bob Subhan Riza; Edy Victor Haryanto; Rika Rosnelly; Mira Kartiwi; Zuriati Janin (Universitas Ahmad Dahlan, 2020)
Emotion recognition using images, videos, or speech as input is considered as a hot topic in the field of research over some years. With the introduction of deep learning techniques, e.g., convolutional neural networks (CNN), applied in emotion recognition, has produced promising results. Human facial expressions are considered as critical components in understanding one's emotions. This paper sheds light on recognizing the emotions using deep learning techniques from the videos. The methodology of the recognition process, along with its description, is provided in this paper. Some of the video-based datasets used in many scholarly works are also examined. Results obtained from different emotion recognition models are presented along with their performance parameters. An experiment was carried out on the fer2013 dataset in Google Colab for depression detection, which came out to be 97% accurate on the training set and 57.4% accurate on the testing set.