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

arXiv:2609.29553v1 Announce Type: cross Abstract: 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 au=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 au=15, while processing all frames at au=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.

Source: arXiv cs.AI | 2026-09-25