2134/36578
Hui Fang
Hui
Fang
Neil Mac Parthalain
Neil Mac
Parthalain
Andrew J. Aubrey
Andrew J.
Aubrey
Gary K.L. Tam
Gary K.L.
Tam
Rita Borgo
Rita
Borgo
Paul L. Rosin
Paul L.
Rosin
Philip W. Grant
Philip W.
Grant
David Marshall
David
Marshall
Min Chen
Min
Chen
Facial expression recognition in dynamic sequences: An integrated approach
Loughborough University
2019
Facial expression analysis
Dynamic feature extraction and visualisation
Artificial Intelligence and Image Processing
Information and Computing Sciences not elsewhere classified
2019-01-14 15:47:10
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Facial_expression_recognition_in_dynamic_sequences_An_integrated_approach/9402908
Automatic facial expression analysis aims to analyse human facial expressions and classify them into discrete categories. Methods based on existing
work are reliant on extracting information from video sequences and employ either some form of subjective thresholding of dynamic information or
attempt to identify the particular individual frames in which the expected
behaviour occurs. These methods are inefficient as they require either additional subjective information, tedious manual work or fail to take advantage
of the information contained in the dynamic signature from facial movements
for the task of expression recognition.
In this paper, a novel framework is proposed for automatic facial expression analysis which extracts salient information from video sequences
but does not rely on any subjective preprocessing or additional user-supplied
information to select frames with peak expressions. The experimental framework demonstrates that the proposed method outperforms static expression
recognition systems in terms of recognition rate. The approach does not rely on action units (AUs) and therefore, eliminates errors which are otherwise
propagated to the final result due to incorrect initial identification of AUs.
The proposed framework explores a parametric space of over 300 dimensions
and is tested with six state-of-the-art machine learning techniques. Such
robust and extensive experimentation provides an important foundation for
the assessment of the performance for future work. A further contribution
of the paper is offered in the form of a user study. This was conducted in
order to investigate the correlation between human cognitive systems and the
proposed framework for the understanding of human emotion classification
and the reliability of public databases.