Feature Extraction Technique for Emotion Detection using Machine Learning
|International Journal of Electronics and Communication Engineering|
|© 2020 by SSRG - IJECE Journal|
|Volume 7 Issue 5|
|Year of Publication : 2020|
|Authors : Kaushal Puri,Devasheesh Tripathi,Yashvi Sudan,Prof. A.B Patil|
How to Cite?
Kaushal Puri,Devasheesh Tripathi,Yashvi Sudan,Prof. A.B Patil, "Feature Extraction Technique for Emotion Detection using Machine Learning," SSRG International Journal of Electronics and Communication Engineering, vol. 7, no. 5, pp. 41-45, 2020. Crossref, https://doi.org/10.14445/23488549/IJECE-V7I5P107
Facial Expression Recognition, due to its wide research areas, becomes an active research topic, and it relies on advancements in Image Processing and Computer Vision techniques. Such systems have a variety of interesting applications, from human-computer interaction to robotics and computer animations. Technologies used in this exemplary project are basic programming languages like Python and machine learning. This project aims at structuring a facial expression recognition system capable of distinguishing the six universal emotions: disgust, anger, fear, happiness, sadness, and surprise in real-time scenarios.
The system uses a Haar cascade classifier for feature extraction, and the Fisherface algorithm uses a uniform Local Binary Pattern. This project's main purpose is to create a healthy work environment for the corporate sector and help patients with Asperger syndrome. This can have wide usage by psychologists as well for treating their patients in therapy.
Facial Recognition, Fisherface algorithm, Local Binary Pattern (LBP), Fisher Linear Discriminant (FDL), FER (Facial expression recognition), PCA (Principal Component Analysis), AAM (Active Appearance Models), PCA (principal component analysis).
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