A Multi-Population FA for Automatic Facial Emotion Recognition

Kamlesh Mistry, Baqar Rizvi, Chris Rook, Sadaf Iqbal, Li Zhang, Colin Paul Joy

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationA Multi-Population FA for Automatic Facial Emotion Recognition
Place of PublicationInternational Joint Conference on Neural Networks (IJCNN)
ISBN (Electronic)9781728169279
ISBN (Print)9781728169262
Publication statusPublished - 28 Sept 2020

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