Work

Facial Expression Recognition using Convolutional Neural Network with Data Augmentation

Year: 2019

Type: conference-paper

Abstract: Detecting emotion from facial expression has become an urgent need because of its immense applications in artificial intelligence such as human-computer collaboration, data-driven animation, human-robot communication etc. Since it is a demanding and interesting problem in computer vision, several works had been conducted regarding this topic. The objective of this research is to develop a facial expression recognition system based on convolutional neural network with data augmentation. This approach enables to classify seven basic emotions consist of angry, disgust, fear, happy, neutral, sad and surprise from image data. Convolutional neural network with data augmentation leads to higher validation accuracy than the other existing models (which is 96.24%) as well as helps to overcome their limitations.

Source: International Conference on Informatics, Electronics & Vision (ICIEV)

DOI: 10.1109/iciev.2019.8858529

Authors: Tawsin Uddin Ahmed, Sazzad Hossain, Mohammad Shahadat Hossain, Raihan ul Islam, Karl Andersson

Institutions: University of Chittagong, University of Liberal Arts Bangladesh, Luleå University of Technology

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W2979356785

FWCI: 15.44

Cites: 27

Cited by: 102

Related to: 10