Statistical, Spectral and Graph Representations for Video-based Facial Expression Recognition in Children
Abbasi, Nida Itrat
2022 IEEE International Conference on Acoustics, Speech and Signal Processing
MetadataShow full item record
Abbasi, N. I., Song, S., & Gunes, H. Statistical, Spectral and Graph Representations for Video-based Facial Expression Recognition in Children. 2022 IEEE International Conference on Acoustics, Speech and Signal Processing. https://doi.org/10.17863/CAM.81242
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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.
W.D.Armstrong Trust Studentship and the Cambridge Trusts. The European Union’s Horizon 2020 Research and Innovation programme under grant agreement No. 826232.
Engineering and Physical Sciences Research Council (EP/R030782/1)
European Commission Horizon 2020 (H2020) Societal Challenges (826232)
Embargo Lift Date
External DOI: https://doi.org/10.17863/CAM.81242
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333822
All Rights Reserved
Licence URL: http://www.rioxx.net/licenses/all-rights-reserved