Recognition of Face Emotion using Convolutional Neural Network
Panyam Narahari Sastry1, Mohammed Sameer Syed2
1Dr. Panyam Narahari Sastry, Professor, Department of ECE, Chaitanya Bharathi Institute of Technology (A), Hyderabad, India.
2Mohammed Sameer Syed, PG Scholar, Department of ECE, Chaitanya Bharathi Institute of Technology (A), Hyderabad, India.
Manuscript received on June 14, 2020. | Revised Manuscript received on June 28, 2020. | Manuscript published on July 10, 2020. | PP: 364-376 | Volume-9 Issue-9, July 2020 | Retrieval Number: 100.1/ijitee.I7033079920 | DOI: 10.35940/ijitee.I7033.079920
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Recognition of face emotion has been a challenging task for many years. This work uses machine learning algorithms for both, a real-time image or a stored database image in the area of facial emotion recognition system. So it is very clear that, deep learning technology becomes important for Human-computer interaction (HCI) applications. The proposed system has two parts, real-time based facial emotion recognition system and also the image based facial emotion recognition system. A Convolutional Neural Network (CNN) model is used to train and test different facial emotion images in this research work. This work was executed successfully using Python 3.7.6 platform. The input Face image of a person was taken using the webcam video stream or from the standard database available for research. The five different facial emotions considered in this work are happy, surprise, angry, sad and neutral. The best recognition accuracy with the proposed system for the webcam video stream is found to be 91.2%, whereas for the input database images is found to be 90.08%.
Keywords: Convolutional Neural Network, Deep Learning, Human Computer Interaction, Machine Learning, Python.
Scope of the Article: Deep Learning