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Fusion: Practice and Applications
Volume 2 , Issue 1, PP: 31-41 , 2020 | Cite this article as | XML | Html |PDF


Ensemble Learning for Facial Expression Recognition

Authors Names :   Anjali Raghav   1 *     Monika Gupta   2  

1  Affiliation :  Maharaja Agrasen Institute of Technology, INDIA

    Email :  anjali.raghav.261@gmail.com

2  Affiliation :  Maharaja Agrasen Institute of Technology, INDIA

    Email :  monikagupta@mait.ac.in

Doi   :   https://doi.org/10.54216/FPA.020104

Received: March 20, 2020 Revised: April 28, 2020 Accepted: May 30, 2020

Abstract :

Facial expressions are the translation of the emotions such as anger, sadness, happiness, disgust felt by a person. Facial expression recognition, classification of expressions which has application in various industries such as hospitality, medical to name a few. There are various datasets available for facial expression recognition, we used FER 2013 dataset to build a classification algorithm. This algorithm classifies the emotions into seven categories namely, angry, disgust, happy, sad, fear, surprise and neutral. In traditional convolutional neural network algorithm the computing time is very large, ensemble learning significantly reduced the computing time and offered a promising accuracy. Features of images were extracted using the convolutional neural network, further these features were implemented using XGBoost and Random Forest to build classification algorithms and an accuracy of 77% and 74% was obtained. This was comparable to the accuracy obtained by traditional convolutional neural network which was 75% also with very less computing time.

Keywords :

Ensemble Learning ; Facial Expression Recognition

References :

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Cite this Article as :
Anjali Raghav , Monika Gupta, Ensemble Learning for Facial Expression Recognition, Fusion: Practice and Applications, Vol. 2 , No. 1 , (2020) : 31-41 (Doi   :  https://doi.org/10.54216/FPA.020104)