用基础模型分析虹膜图像，预测执勤警觉性
Can Foundation Models Predict Fitness for Duty?
- 基于自监督基础模型，从虹膜图像中学习警觉性特征
- 无需大量标注数据即可实现执勤状态预测
- 适合安全监控、疲劳检测等实际场景应用
生物识别设备通过近红外虹膜图像评估人员警觉性，已超越单纯身份识别。然而，收集大量与饮酒、用药及睡眠不足相关的图像以训练AI模型仍具挑战性。通常深度学习需海量图像才能有效训练。借助自监督基础模型的泛化能力，现可通过少量数据训练下游模型，极大提升该领域的可行性。本文研究深度学习与基础模型在预测执勤状态（即工作警觉性）中的应用，验证其在真实场景下的潜力。
原文摘要 · Abstract (English)
Biometric capture devices have been utilised to estimate a person's alertness through near-infrared iris images, expanding their use beyond just biometric recognition. However, capturing a substantial number of corresponding images related to alcohol consumption, drug use, and sleep deprivation to create a dataset for training an AI model presents a significant challenge. Typically, a large quantity of images is required to effectively implement a deep learning approach. Currently, training downstream models with a huge number of images based on foundational models provides a real opportunity to enhance this area, thanks to the generalisation capabilities of self-supervised models. This work examines the application of deep learning and foundational models in predicting fitness for duty, which is defined as the subject condition related to determining the alertness for work.
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