Abstract
Alcohol consumption is a significant public health concern and a major cause of accidents and fatalities worldwide. This study introduces a novel video-based facial sequence analysis approach dedicated to the detection of alcohol intoxication. The method integrates facial landmark analysis via a Graph Attention Network (GAT) with spatiotemporal visual features extracted using a 3D ResNet. These features are dynamically fused with adaptive prioritization to enhance classification performance. Additionally, we introduce a curated dataset comprising 3,542 video segments derived from 202 individuals to support training and evaluation. Our model is compared against two baselines: a custom 3D-CNN and a VGGFace+LSTM architecture. Experimental results show that our approach achieves 95.82% accuracy, 0.977 precision, and 0.97 recall, outperforming prior methods. The findings demonstrate the model's potential for practical deployment in public safety systems for non-invasive, reliable alcohol intoxication detection.
Abstract (translated)
酒精消费是一个重要的公共卫生问题，也是全球事故和死亡的主要原因。本研究提出了一种基于视频的面部序列分析方法，专门用于检测酒精中毒。该方法结合了通过图注意力网络（GAT）进行的面部标志点分析以及使用3D ResNet提取的空间时间视觉特征。这些特征被动态融合并采用自适应优先级以提高分类性能。此外，我们引入了一个经过精心策划的数据集，包括来自202个个体的3,542段视频片段，用于支持训练和评估。我们的模型与两个基准进行了比较：一个定制的3D-CNN架构和VGGFace+LSTM架构。实验结果显示，我们的方法实现了95.82%的准确率、0.977的精确度和0.97的召回率，超过了先前的方法。研究结果表明，该模型在公共安全系统中具有非侵入性和可靠地检测酒精中毒的实际部署潜力。
URL
https://arxiv.org/abs/2512.04536
