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Fusion: Practice and Applications
Volume 2 , Issue 1, PP: 22-30 , 2020 | Cite this article as | XML | Html |PDF


Identification of Facial Expressions using Deep Neural Networks

Authors Names :   Harsh Jain   1 *     Parv Bharti   2     Arun Kumar Dubey   3     Preetika Soni   4  

1  Affiliation :  Information Technology Bharati Vidyapeeth's College of Engg, New Delhi, India

    Email :  harshjain2525@gmail.com

2  Affiliation :  Information Technology Bharati Vidyapeeth's College of Engg, New Delhi, India

    Email :  parv.bharti@gmail.com

3  Affiliation :  Information Technology Bharati Vidyapeeth's College of Engg, New Delhi, india;

    Email :  arudubey@gmail.com

4  Affiliation :  Information Technology Bharati Vidyapeeth's College of Engg, New Delhi, India

    Email :  sonipreetika20@gmail.com

Doi   :   https://doi.org/10.54216/FPA.020101

Received: March 13, 2020 Revised: April 18, 2020 Accepted: May 10, 2020

Abstract :

Detecting and analyzing emotions from human facial movements is a problem defined and developed over many years for the benefits it brings. During playback, when developing data sets, data sets with methods become more and more complex, and accuracy and difficulty increase gradually. In the given paper, we will use a deep structured learned network using the two mechanisms - Vgg and Resnet50 with deep layers to classify emotions based on input images in complex environments. Besides that, we also use learning methods combining many modern models to increase accuracy. Experimental results show that the two proposed methods have better results than some modern methods in emotional recognition problems for complex input images and some results reported in scientific studies. Particularly combined learning method gives good accuracy - 66.15% on the dataset FER2013

Keywords :

Facial expression; Deep Neural Network; VGG , Resnet;

References :


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Cite this Article as :
Harsh Jain , Parv Bharti , Arun Kumar Dubey , Preetika Soni, Identification of Facial Expressions using Deep Neural Networks, Fusion: Practice and Applications, Vol. 2 , No. 1 , (2020) : 22-30 (Doi   :  https://doi.org/10.54216/FPA.020101)