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Emotions Detection using Java and Neural Networks

Ayeni Olaniyi Abiodun, Mogaji Stephen Alaba, Olayemi Olufunke C.. Published in Artificial Intelligence.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Ayeni Olaniyi Abiodun, Mogaji Stephen Alaba, Olayemi Olufunke C.

Ayeni Olaniyi Abiodun, Mogaji Stephen Alaba and Olayemi Olufunke C.. Emotions Detection using Java and Neural Networks. Communications on Applied Electronics 6(1):58-63, October 2016. BibTeX

	author = {Ayeni Olaniyi Abiodun and Mogaji Stephen Alaba and Olayemi Olufunke C.},
	title = {Emotions Detection using Java and Neural Networks},
	journal = {Communications on Applied Electronics},
	issue_date = {October 2016},
	volume = {6},
	number = {1},
	month = {Oct},
	year = {2016},
	issn = {2394-4714},
	pages = {58-63},
	numpages = {6},
	url = {},
	doi = {10.5120/cae2016652415},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


This research work focuses on the design and implementation of a facial expression detection system focusing mainly on three expressions which are happiness, anger and sadness. Every image is received as jpeg files, Images are then preprocessed forl feature extractions. Feature extraction on each images received is extracted using principal component analysis (PCA), extracted features are passed to the neural networks. Java object oriented programming language and support vector machines (SVM) algorithm was used in the design and implementation of the system. In this research work, facial expression detecting system is implemented using java programming language and SVM algorithm. The project introduces a simple architecture for human facial expression detection and elaborate more on what facial expression and facial expression detection system is about. The project looks into the existing system, identifies the problem thereby deploying a new system which is user interactive. The system developed achieved a good result of 81.25% for happiness, 75% for anger and 60% for sadness.


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Facial expression, images, emotions, detection, recognition, classification, java programming language and system