A SYSTEMATIC REVIEW OF FACIAL EXPRESSION DETECTION METHODS

A SYSTEMATIC REVIEW OF FACIAL EXPRESSION DETECTION METHODS

S. Sathya, M. E 1, B.Arun kumar2, k.Karuppasamy3, M.Nithishkumar4 , R.Shabari anand5 .

S Sathya, M. E1 Department of Computer Science and Engineering

Hindusthan College of Engineering and Technology

E-mail: sathya.cse@hicet.ac.in

B.Arun kumar2 Department of Computer Science and Engineering

Hindusthan College of Engineering and Technology

E. mail: 20104078@hicet.ac.in

k.Karuppasamy3 Department of Computer Science and Engineering

Hindusthan College of Engineering and Technology

E. mail: 20104105@hicet.ac.in

M.Nithishkumar4 Department of Computer Science and Engineering

Hindusthan College of Engineering and Technology

E. mail: 20104126@hicet.ac.in

R.Shabari anand5 Department of Computer Science and Engineering

Hindusthan College of Engineering and Technology

E. mail: 20104810@hicet.ac.in

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ABSTRACT - Facial expression analysis aims to understand human emotions by analyzing visual face information and is a popular topic in the computer vision community. In educational research, the analyzed students’ affect states can be used by faculty members as feedback to improve their teaching style and strategy so that the learning rate of all the students present can be enhanced. Facial expression analysis has attracted much attention in educational research, and a few reviews on this topic have emerged. However, previous reviews on facial expression recognition methods in educational research focus mostly on summarizing the existing literature on emotion models from a theoretical perspective, neglecting technical summaries of facial expression recognition. In order to advance the development of facial expression analysis in educational research, this paper outlines the tasks, progress, challenges, and future trends related to facial expression analysis. First, facial expression recognition methods in educational research lack an overall framework.Second, studies based on the latest machine learning methods are not mentioned in previous reviews.Understanding emotions is one of the greatest capabilities of human beings, as it allows the understanding of facial expressions that facilitate the capture of important information about other individuals, which are used for the perception of mental or emotional states. Advances in Artificial Intelligence and Visual Computing, more specifically in Deep Learning with the advent of Artificial Neural Networks, have enhanced the ability of machines to infer human emotions through face expression.The convolutional neural network models analyzed in this review are based on deep learning with an emphasis on expression and microexpression recognition. The results suggest that database uses, with laboratory controlled images, combined with CNN’s such as VGG and ResNet, have excellent performances in their tests. For better understanding, we will detail and compare all the methods obtained in the review.