KGLA-Net: an efficient facial affective computing framework for psychomotor phenotyping in psychiatry.

Li F, Yu J, Sun M, Li X

Open source

DOI: 10.3389/fpsyt.2026.1903055
Published: 2026
Container: Frontiers in psychiatry
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fpsyt.2026.1903055,
  title = {KGLA-Net: an efficient facial affective computing framework for psychomotor phenotyping in psychiatry.},
  author = {Li F and Yu J and Sun M and Li X},
  year = {2026},
  journal = {Frontiers in psychiatry},
  doi = {10.3389/fpsyt.2026.1903055},
  url = {https://doi.org/10.3389/fpsyt.2026.1903055}
}

RIS

TY  - JOUR
TI  - KGLA-Net: an efficient facial affective computing framework for psychomotor phenotyping in psychiatry.
AU  - Li F
AU  - Yu J
AU  - Sun M
AU  - Li X
PY  - 2026
JO  - Frontiers in psychiatry
DO  - 10.3389/fpsyt.2026.1903055
UR  - https://doi.org/10.3389/fpsyt.2026.1903055
ER  -

APA

F, L., J, Y., M, S., & X, L. (2026). KGLA-Net: an efficient facial affective computing framework for psychomotor phenotyping in psychiatry.. Frontiers in psychiatry. https://doi.org/10.3389/fpsyt.2026.1903055

Source records

pubmed · retrieved 2026-09-25T06:31:56.584Z
europe-pmc · retrieved 2026-09-25T06:31:56.571Z
doaj · retrieved 2026-09-25T06:31:56.580Z