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UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing — arXiv:2609.29553

Source

arXiv (en)

Original

https://arxiv.org/abs/2609.29553

License (as archived)

CC0-1.0

Archived

2026-10-08T04:27:36.510Z

SHA-256 of the archived text

2df4b14c1f8ab020a7488d0edf594e797fd69c4d3c4252652fb8946ff3758d00

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https://metraipolis.com/documento/dc_0bbde4cd51bb

How to cite

UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing — arXiv:2609.29553 — arXiv (en), https://arxiv.org/abs/2609.29553 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_0bbde4cd51bb (Metraipolis archive dc_0bbde4cd51bb, sha256 2df4b14c1f8ab020a7488d0edf594e797fd69c4d3c4252652fb8946ff3758d00)

@misc{dc0bbde4cd51bb,
  title = {UNWIND: Any-Length Facial Video for Stress Detection without Temporal Windowing — arXiv:2609.29553},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.29553},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_0bbde4cd51bb; sha256 2df4b14c1f8ab020a7488d0edf594e797fd69c4d3c4252652fb8946ff3758d00},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.29553. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Automatic stress recognition from facial video provides a non-contact approach for affective monitoring. However, most existing video-based methods divide complete recordings into shorter temporal segments before performing classification. Such segmentation requires additional decisions concerning segment duration, overlap, and prediction aggregation, and may restrict the model from exploiting information distributed across the entire recording. We introduce UNWIND, a facial-video framework for stress detection that analyzes a complete recording as a single model input, eliminating the need for temporal windowing or external segmentation. UNWIND reorganizes the video by folding its temporal dimension into the channel dimension of a two-dimensional spatial representation, which is subsequently processed through a unified asymmetric-attention architecture. With a temporal stride of $\tau=1$, the framework processes the entire $120$-second sequence, corresponding to $3{,}600$ frames sampled at $30$~fps, in a single input. We evaluate seven temporal-stride settings on a stress dataset comprising $58$ subjects, using a stratified subject-level protocol that covers configurations from dense frame retention to sparse temporal sampling. The highest test accuracy, $70.02\%$, is obtained at $\tau=15$, while processing all frames at $\tau=1$ achieves a comparable accuracy of $69.73\%$. Computational requirements range from $12.48$ to $348.78$ GFLOPs across the evaluated stride settings, illustrating the balance between temporal sampling density and computational efficiency. The findings show that effective facial-video stress recognition can be achieved without dividing recordings into temporal windows and that complete-recording inference can be performed within a single unified model.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-25 in cs.AI).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2609.29553 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T03:50:58.098Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-09-25?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).