Deep learning methods for facial expression recognition

Mohammad Masum Refat, Chowdhury and Zainul Azlan, Norsinnira (2019) Deep learning methods for facial expression recognition. In: "7th International Conference on Mechatronics Engineering, ICOM 2019", 30 - 31 Oct. 2019, Putrajaya, Malaysia.

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Abstract

Deep learning is very popular methods for facial expression recognition (FER) and classification. Different types of deep learning algorithms have been used for FER such as deep belief network (DBN) and convolutional neural network (CNN). In this paper, we analyze various deep learning methods and their results. We have chosen Deep convolutional neural network as the best algorithms for facial expression detection and classification. In our study, we have tested the algorithm using Japanese Female facial expressions database (JAFFE) datasets by anaconda software. The deep convolution neural networks with JAFFE datasets accuracy rate around 97.01%.

Item Type:	Conference or Workshop Item (Invited Papers)
Additional Information:	4494/79711
Uncontrolled Keywords:	Convolutional neural networks (CNN), deep belief network (DBN), Facial expression recognitions (FER), facial expression classification.
Subjects:	T Technology > T Technology (General) T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechatronics Engineering
Depositing User:	Norsinnira Zainul Azlan
Date Deposited:	17 Jun 2020 15:49
Last Modified:	13 Jul 2020 10:18
URI:	http://irep.iium.edu.my/id/eprint/79711

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