Using neural and distance-based machine learning techniques in order to identify genuine and acted emotions from facial expressions

RANNOW BUDKE, Jaine and DA COSTA ABREU, Marjory (2021). Using neural and distance-based machine learning techniques in order to identify genuine and acted emotions from facial expressions. In: 11th International Conference of Pattern Recognition Systems (ICPRS 2021). IET, 121-126.

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Abstract

Facial expressions are part of human non-verbal communication. Automatically discriminating between genuine and acted emotion can help psychologists, judges, human-machine interface, and so on. The problems for researchers starts when there are few real emotion facial datasets available, and thus, most of experimentation for evaluation is done by using fake emotions from actors. Thus, this paper explores the problem of classifying emotions from facial expressions as genuine or acted. We propose to extract facial features from images and to classify using k-Means, k-Nearest Neighbor and Neural Network. The best results obtained presented a promising 98.6% of precision for happiness emotion and 92% for sadness emotion.

Item Type:	Book Section
Additional Information:	11th International Conference of Pattern Recognition Systems 17-19 March 2021
Identification Number:	https://doi.org/10.1049/icp.2021.1433
Page Range:	121-126
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	22 Jan 2021 17:00
Last Modified:	26 Oct 2021 14:00
URI:	https://shura.shu.ac.uk/id/eprint/28010

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