Emotion Detection on live video using Deep Learning
Krishnaiah Boyana1, Venkateswara Rao Gurrala2, Bhaskar Rao Koutharapu3, Ratna Prakash Pedapudi4, SK. Mabasha5
1Krishnaiah Boyana*, Department of Information Technology, Bapatla Engineering College, Guntur, India.
2Dr. Venkateswara Rao Gurrala, Department of Information Technology, GIT, Gitam Deemed to be University Visakhapatnam, India.
3Bhaskar Rao Koutharapu, Department of Information Technology, Bapatla Engineering College, Guntur, India.
4Ratna Prakash Pedapudi, Department of Information Technology, Bapatla Engineering College, Guntur, India.
5SK. Mabasha, Department of Information Technology, Bapatla Engineering College, Guntur, India.
Manuscript received on August 19, 2020. | Revised Manuscript received on August 26, 2020. | Manuscript published on September 10, 2020. | PP: 74-77 | Volume-9 Issue-11, September 2020 | Retrieval Number: 100.1/ijitee.J75760891020| DOI: 10.35940/ijitee.J7576.0991120
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: In modern days, feeling exposure is a ground of curiosity and is used in fields such as cross-examining prisoners and teenagers observing human-computer relations. The anticipated work designates the exposure of mortal sentiments from an instantaneous video or stationary video with the help of a convolution neural network (CNN) and haar cascade algorithm. The foremost part of the announcement constitutes field appearance. The suggested work aims to categorize a given video or a live video into one of the emotions (natural, angry, happy, fearful, disgusted, sad, surprise). Our work also distinguishes multiple faces from live video and organize their emotions. Our recommended work also imprisonments the pictures from the video every second, hoard them into a file, and generates a video from those pictures along with their respective.
Keywords: Convolutional Neural Networks, Human-Computer interaction, Artificial Neural Networks.
Scope of the Article: Deep Learning