A Multi-Population FA for Automatic Facial Emotion Recognition

Mistry, Kamlesh, Rizvi, Baqar, Rook, Chris, Iqbal, Sadaf, Zhang, Li and Joy, Colin Paul (2020) A Multi-Population FA for Automatic Facial Emotion Recognition. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, Piscataway, NJ. ISBN 9781728169262 ; 9781728169279

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Official URL: http://dx.doi.org/10.1109/IJCNN48605.2020.9207516


Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models.

Item Type: Book Section
Subjects: G400 Computer Science
G500 Information Systems
G700 Artificial Intelligence
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 12 Oct 2020 12:33
Last Modified: 31 Jul 2021 13:02
URI: http://nrl.northumbria.ac.uk/id/eprint/44482

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