STATISTICAL, SPECTRAL AND GRAPH REPRESENTATIONS FOR VIDEO-BASED FACIAL EXPRESSION RECOGNITION IN CHILDREN
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Authors
Abbasi, NI
Song, S
Gunes, H
Publication Date
2022Journal Title
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Conference Name
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISSN
1520-6149
ISBN
9781665405409
Publisher
IEEE
Volume
2022-May
Pages
1725-1729
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Abbasi, N., Song, S., & Gunes, H. (2022). STATISTICAL, SPECTRAL AND GRAPH REPRESENTATIONS FOR VIDEO-BASED FACIAL EXPRESSION RECOGNITION IN CHILDREN. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2022-May 1725-1729. https://doi.org/10.1109/ICASSP43922.2022.9747102
Abstract
Child facial expression recognition is a relatively less investigated area within affective computing. Children's facial expressions differ significantly from adults; thus, it is necessary to develop emotion recognition frameworks that are more objective, descriptive and specific to this target user group. In this paper we propose the first approach that (i) constructs video-level heterogeneous graph representation for facial expression recognition in children, and (ii) predicts children's facial expressions using the automatically detected Action Units (AUs). To this aim, we construct three separate length-independent representations, namely, statistical, spectral and graph at video-level for detailed multi-level facial behaviour decoding (AU activation status, AU temporal dynamics and spatio-temporal AU activation patterns, respectively). Our experimental results on the LIRIS Children Spontaneous Facial Expression Video Database demonstrate that combining these three feature representations provides the highest accuracy for expression recognition in children.
Keywords
Affect Recognition, Child Facial Expressions, Heterogeneous Graph Representation, Deep Learning
Sponsorship
Engineering and Physical Sciences Research Council (EP/R030782/1)
European Commission Horizon 2020 (H2020) Societal Challenges (826232)
Embargo Lift Date
2023-05-23
Identifiers
External DOI: https://doi.org/10.1109/ICASSP43922.2022.9747102
This record's URL: https://www.repository.cam.ac.uk/handle/1810/338272
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