Fourier Series Model for Facial Feature Point Land-Marking

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dc.contributor.authorArabian H
dc.contributor.authorDing N
dc.contributor.authorChase, Geoff
dc.contributor.authorMoeller K
dc.date.accessioned2024-07-04T01:03:01Z
dc.date.available2024-07-04T01:03:01Z
dc.date.issued2023
dc.description.abstractThe field of digital health apps, combined with intelligent learning systems, is new and expanding to incorporate a wide range of possibilities in different domains. An application in the field of digital therapy is for the incorporation of emotion recognition systems as a tool for therapeutic interventions. Adopting an individually tailored virtual world combined with a novel reward system in a gaming scenario, complemented with the technical affinity of most autism spectrum disorder (ASD) children makes a suitable atmosphere for therapeutic intervention. In this paper the use of image processing techniques coupled with Fourier models is used to generate point land-mark annotations on facial features in an image. The OULU-CASIA database was used for the analysis process. The images were first pre-processed based on previous work to reduce background noise and focus on the face. Afterwards a de-correlation stretch was executed to separate different features. A series of morphological, region detections and boundary traces followed. Fourier series models were used to transition the rough segmented pixel data into a smooth geometric representation. Twenty evenly distributed land-mark points are then selected from a fine mesh. Results showed that the geometric representation adhered to the segmented pixel data with a mean of 81.88% Dice similarity. The positive outlook highlighted the effectiveness of such a technique in automating the land-mark annotation process, which is tedious and time consuming. This method leads to explainable machine learning feature representations, which lead to more robust emotion recognition models.
dc.identifier.citationArabian H, Ding N, Chase JG, Moeller K (2023). Fourier Series Model for Facial Feature Point Land-Marking. IFAC-PapersOnLine. 56. 2. 7354-7358.
dc.identifier.doihttp://doi.org/10.1016/j.ifacol.2023.10.350
dc.identifier.isbn9781713872344
dc.identifier.issn2405-8963
dc.identifier.urihttps://hdl.handle.net/10092/107168
dc.publisherElsevier BV
dc.rightsCopyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license.
dc.rights.urihttp://hdl.handle.net/10092/17651
dc.subjectAutism Spectrum Disorder
dc.subjectDigital health
dc.subjectEmotion recognition
dc.subjectFourier series
dc.subjectGeometric feature representation
dc.subjectImage land-marking
dc.subjectTherapeutic application
dc.subject.anzsrc46 - Information and computing sciences::4608 - Human-centred computing::460802 - Affective computing
dc.subject.anzsrc46 - Information and computing sciences::4608 - Human-centred computing::460806 - Human-computer interaction
dc.subject.anzsrc46 - Information and computing sciences::4607 - Graphics, augmented reality and games::460706 - Serious games
dc.subject.anzsrc46 - Information and computing sciences::4601 - Applied computing::460102 - Applications in health
dc.titleFourier Series Model for Facial Feature Point Land-Marking
dc.typeConference Contributions - Published
uc.collegeFaculty of Engineering
uc.departmentMechanical Engineering
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