Facial Expression Recognition using Deep Learning
Suhail Ahmed1, S. Ponmaniraj2

1Suhail Ahmed*, UG Student, School of Computing Science and Engineering, Galgotias University, Greeater Noida.
2S. Ponmaniraj, Assistant Professor, School of Computing Science and Engineering, GalgotiasUniversity, Greater Noida.

Manuscript received on March 28, 2020. | Revised Manuscript received on April 25, 2020. | Manuscript published on April 30, 2020. | PP: 1845-1849 | Volume-9 Issue-4, April 2020. | Retrieval Number: D8901049420/2020©BEIESP | DOI: 10.35940/ijeat.D8901.049420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Facial expression recognition (FER) is now getting extensively popular because of its ability to predict an unknown data-set, and to its extent with some accuracy. An average human being possesses or shows seven different expressions based on the situation, namely anger, sad, happy, surprise, disgust, neutral and scared. Each individual has a unique way to express the afore-mentioned expressions and hence the term “an unknown data-set”. To identify human’s present mindset through facial expressions, many data sets are prepared based on face components (such as lips, cheek, nose, eyes and eye brows etc.,) dislocations and elasticity of all the facial parts. Many facial recognition systems are functioning on muscle distribution analysis from the mother image set’s pixel parameters. This research paper is going to present about image pre processing, facial expression learning methods, classification methods and implementation of FaceEx algorithm for facial expression analysis through FER2013 CNN data sets and Viola-Jones Principle.
Keywords: Convolutional Neural Network, Deep Belief Network, Facial Expression Recognition, Face Normalization, Gabor Wavelet Form, Local Response Normalization.