Expression Analysis System
Riya Kalburgi1, Punit Solanki2, Rounak Suthar3, Saurabh Suman4

1Riya Kalburgi*, Information Technology, Shree L.R. Tiwari College of Engineering, Thane, India.
2Punit Solanki, Information Technology, Shree L.R. Tiwari College of Engineering, Thane, India.
3Rounak Suthar, Information Technology, Shree L.R. Tiwari College of Engineering, Thane, India.
4Saurabh Suman, Information Technology, Shree L.R. Tiwari College of Engineering, Thane, India.

Manuscript received on December 28, 2021. | Revised Manuscript received on January 03, 2021. | Manuscript published on February 28, 2021. | PP: 13-15 | Volume-10 Issue-3, February 2021. | Retrieval Number: 100.1/ijeat.C21280210321 | DOI: 10.35940/ijeat.C2128.0210321
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© 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: Expression is the most basic personality trait of an individual. Expressions, ubiquitous to humans from all cultures, can be pivotal in analyzing the personality which is not confined to boundaries. Analyzing the changes in the expression of the individual can bolster the process of deriving his/her personality traits underscoring the paramount reactions like anger, happiness, sadness and so on. This paper aims to exercise Neural Network algorithms to predict the personality traits of an individual from his/her facial expressions. In this paper, a methodology to analyze the personality traits of the individual by periodic monitoring of the changes in facial expressions is presented. The proposed system is intended to analyze the expressions by exploiting Neural Networks strategies to first analyze the facial expressions of the individual by constantly monitoring an individual under observation. This monitoring is done with the help of OpenCV which captures the facial expression at an interval of 15 secs. Thousands of images per expression are used to train the model to aptly distinguish between expression using prominent Neural Network Methodologies of Forward and Backward Propagation. The identified expression is then be fed to a derivative system which plots a graph highlighting the changes in the expression. The graph acts as the crux of the proposed system. The project is important from the perspective of serving as an alternative to manual monitoring which are prone to errors and subjective in nature. 
Keywords: Backward Propagation, Expression Analysis, Expression Analysis System, Forward Propagation, Neural Networks, Open CV.