Facial Expression Recognition using Deep Learning

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2019 by IJETT Journal
Volume-67 Issue-3
Year of Publication : 2019
Authors : Achuthan Babu V G, Sureshkumar A, Suresh Babu P
  10.14445/22315381/IJETT-V67I3P225

MLA 

MLA Style: Achuthan Babu V G, Sureshkumar A, Suresh Babu P "Facial Expression Recognition using Deep Learning" International Journal of Engineering Trends and Technology 67.3 (2019): 131-134.

APA Style: Achuthan Babu V G, Sureshkumar A, Suresh Babu P (2019). Facial Expression Recognition using Deep Learning. International Journal of Engineering Trends and Technology, 67(3), 131-134.

Abstract
Facial Expression Recognition is one of the challenging problem in computer vision. It is a tedious process in Machine learning because each person shows their expression in unique way. The Deep Learning Algorithms are used for recognize the seven basic expressions of the humans Anger, Sad, Happy, Scared, Suprise, Disgust, Neutral. In this paper, we use the Convolutional Neural Networks(CNN). Convolutional Neural Networks are the mostly used method for overcoming the difficulties during the feature extraction of the Facial Expression Recognition. Here Visual Geometry Group(VGG) model is used for the construction of CNN. For the evaluation we use the FER2013 database. KAZE feature parameters are used for the feature extraction from the images.

Reference
[1] Abir Fathallah, Lofti Abdi, Ali Douik., ”Facial Expression Recognition via Deep Learning”, 2017 IEEE/ACS 14th AICCSA.
[2] Alexandru Savoiu, James Wong, “Recognizing Facial Expression Using Deep Learning. “, Jul 2017.
[3] Raghuvanshi, Vivek Choksi, “Facial Expression Recognition with Convolutional Neural Networks” published on Semantic scholar 2016.
[4] Shan Li , Weihong Deng. “Deep Facial Expression Recognition:Asurvey” published 2018.
[5] Sivaiah Bellamkonda, N.P.Gopalan,” A Facial Expression Recognition Model using Support Vector Machines” publsiehd on (IJMSC) April 2018.
[6] Tom McLaughlin, Mai Le, Naran Bayanbat,” Emotion Recognition with Deep-Belief Networks”, September 2017.
[7] Ping Liu, Shizhong Ha, Zibo Meng, Yan Tong, “Facial Expression Recognition via a Boosted Deep Belief Network” Published in 2014 IEEE Conference on Computer Vision and Pattern Recognition.
[8] Sonali V.Hedaoo , M.D.Katkar , S.P.Khandait, “Feature Tracking and Expression Recognition of Face Using Dynamic Bayesian Network “ (IJETT) International Journ al of Engineering Trends and Technology, (ISSN: 2231 -5381) 10 Feb 2014.
[9] Kaggle Dataset. https://www.kaggle.com/deadskull7/fer2013

Keywords
Facial Expression; Recognition; CNN; Architecture; FER2103; KAZE features.