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Classification of Facial Expressions using Machine Learning

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Authors:
Vatsal Patel, Pratik Kanani
10.5120/ijca2021921599

Vatsal Patel and Pratik Kanani. Classification of Facial Expressions using Machine Learning. International Journal of Computer Applications 183(23):23-28, September 2021. BibTeX

@article{10.5120/ijca2021921599,
	author = {Vatsal Patel and Pratik Kanani},
	title = {Classification of Facial Expressions using Machine Learning},
	journal = {International Journal of Computer Applications},
	issue_date = {September 2021},
	volume = {183},
	number = {23},
	month = {Sep},
	year = {2021},
	issn = {0975-8887},
	pages = {23-28},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume183/number23/32068-2021921599},
	doi = {10.5120/ijca2021921599},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Recognition of facial expressions is one of the most powerful and challenging tasks in Non-verbal communication. Normally major part of communication involves verbal Channels. But Non-verbal gestures are majorly expressed through facial expressions. Our project is based on classification of various human expressions using various types of Face Expression Recognition (FER) techniques which include the three major stages such as preprocessing, feature extraction and classification. We have carried out all these techniques using Convolutional Neural Networks (CNN). Our project is inspired by VGG and Xception model. Datasets used are FER 2013 (for emotion classification), IMDB (for gender classification), FEC (Google facial expression comparison). Using CNN, we classify 7 different expressions like Happy, Sad, Anger, Disgust, Fear, Surprise and Neutral.

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Keywords

Preprocessing, Feature Extraction, CNN, VGG-16, Xception Model, Transfer Learning