JOURNAL ARTICLE

Facial emotion recognition system through machine learning approach

Abstract

Data mining also sometimes called data or knowledge discovery is the process of analyzing data from different perspectives and summarizing it into useful information. Image processing is related to Computer vision, which is a high-level image processing out of which a machine/computer/software intends to decipher the physical contents of an image or a sequence of images. One of the ways to do this is by comparing selected facial features from the image and a facial database. Recognizing emotion from images has become one of the active research themes in image processing and in applications based on human-computer interaction. This research conducts an experimental study on recognizing facial emotions. The flow of our emotion recognition system include the basic process in FER system. These include image acquisition, preprocessing of an image, face detection, feature extraction, classification and then when the emotions are classified the system assigns the user particular music according to his emotion. Our system focuses on live images taken from the webcam. The aim of this research is to develop automatic facial emotion recognition system for stressed individuals thus assigning them music therapy so as to relief stress. The emotions considered for the experiments include happiness, Sadness, Surprise, Fear, Disgust, and Anger that are universally accepted.

Keywords:
Sadness Computer science Artificial intelligence Disgust Surprise Emotion classification Feature extraction Preprocessor Facial recognition system Face (sociological concept) Happiness Anger Facial expression Computer vision Pattern recognition (psychology) Psychology

Metrics

36
Cited By
0.77
FWCI (Field Weighted Citation Impact)
13
Refs
0.75
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Emotion and Mood Recognition
Social Sciences →  Psychology →  Experimental and Cognitive Psychology
Face and Expression Recognition
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
EEG and Brain-Computer Interfaces
Life Sciences →  Neuroscience →  Cognitive Neuroscience
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