Deep learning based emotion recognition for image and video signals: matlab implementation

Ashraf, Arselan and Gunawan, Teddy Surya and Kartiwi, Mira (2021) Deep learning based emotion recognition for image and video signals: matlab implementation. LAP LAMBERT Academic Publishing. ISBN 978-620-3-58356-4

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Abstract

Emotion recognition utilizing pictures, videos, or speech as input is considered an intriguing issue in the research field over certain years. The introduction of deep learning procedures like the Convolutional Neural Networks (CNN) has made emotion recognition achieve promising outcomes. This book is carried out to develop an image and video-based emotion recognition model using CNN for automatic feature extraction and classification with Matlab sample codes. Five emotions are considered for recognition: angry, happy, neutral, sad, and surprise, compared to previous algorithms. Different pre-processing steps have been carried over data samples, followed by the popular and efficient Viola-Jones algorithm for face detection. Evaluating results using confusion matrix, accuracy, F1-score, precision, and recall shows that video-based datasets obtained more promising results than image-based datasets.

Item Type:	Book
Uncontrolled Keywords:	Emotion recognition, convolutional neural networks, image/video database
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering Kulliyyah of Information and Communication Technology > Department of Information System Kulliyyah of Information and Communication Technology > Department of Information System
Depositing User:	Prof. Dr. Teddy Surya Gunawan
Date Deposited:	22 Apr 2021 10:49
Last Modified:	22 Apr 2021 10:49
URI:	http://irep.iium.edu.my/id/eprint/89225

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