Facial Emotion Detection Using Deep Learning: A Survey

Title

Facial Emotion Detection Using Deep Learning: A Survey

Creator

Ansari S.; Kulkarni P.; Rajesh T.; Gurudas V.R.

Description

The long history of facial expression analysis has influenced current research and public interest. The scientific study and comprehension of emotion are credited to Charles Darwin's 19th-century publication The Representation of the Sentiment in Man and Animals (originally published in 1872). As Recognition of human emotions from images is one of the utmost important and difficult societal connection study assignments. One advantage of using a deep learning strategy is its independence from human intervention while undertaking feature engineering. This approach involves an algorithm that scans the data for features that connect, then combines them to promote quicker learning without being explicitly told to. Deep learning (DL) based emotion detection outperforms traditional image processing methods in terms of performance. In this analytical study, the creation of an artificial intelligence (AI) system that can recognize emotions from facial expressions is presented. It discusses the various techniques for doing so, which generally involve three steps: face uncovering, feature extraction, and sentiment categorization. This study describes the various existing solutions and methodologies used by the researchers to build facial landmark interpretation. The Significance of this survey paper is to analyze the recent works on facial expression detection and distribute better insights to novice researchers for the upgradation in this domain. 2023 IEEE.

Source

Proceedings of IEEE InC4 2023 - 2023 IEEE International Conference on Contemporary Computing and Communications

Date

2023-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Convolutional Neural Network; Deep Learning; Facial Emotion

Coverage

Ansari S., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Kulkarni P., Dayanand Sagar University, Department of Computer Science and Engineering, Bangalore, India; Rajesh T., Dayanand Sagar University, Department of Computer Science and Engineering, Bangalore, India; Gurudas V.R., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 979-835033577-4

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/InC457730.2023.10262904

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