Real Time Detection and Identification of Human Emotions through Live Streaming
Keerthana B.Chigateri*, Computer Science department, N.M.A.M.I.T affiliated to Visvesvaraya Technological University, Nitte,Udupi District, Karnataka, India.
Manuscript received on November 17., 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 12842-12845 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9056118419/2019©BEIESP | DOI: 10.35940/ijrte.D9056.118419
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© 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: Automating the analysis of facial expressions of individuals is one of the challenging tasks in opinion mining. In this work, the proposed technique for identifying the face of an individual and the emotions, if present from a live camera. Expression detection is one of the sub-areas of computer visions which is capable of finding a person from a digital image and identify the facial expression which are the key factors of non-verbal communication. Complexity involves mainly in two cases viz., 1)if more than one emotions coexist on a face. 2) expressing same emotion between individuals is not exactly same. Our aim was to make the processes automatic by identify the expressions of people in a live video. In this system OpenCV library containing face recognizer module for detecting the face and for training the model. It was able to identify the seven different expressions with 75-85% accuracy. The expressions identified are happy, sadness, disgust, fear, anger, surprise and neutral. The this an image frame from is captured from the video, locate the face in it and then test it against the training data for predicting the emotion and update the result. This process is continued till the video input exists. On top of this the data set for training should be in such a way that , it prediction should be independent of age, gender, skin color orientation of the human face in the video and also the lamination around the subject of reference.
Keywords: Computer Vision, Facial Emotion Recognition, Human Emotions, Automatic Identification, HAAR Classifier, Face Detection, Live Detection.
Scope of the Article: Real-Time Information Systems.