Multimodal Context-Based Continuous Authentication
Multimodal Context-Based Continuous Authentication
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DOI：
10.1109/ijcb57857.2023.10448626
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Saandeep Aathreya;Meghna Chaudhary;T. Neal;Shaun J. Canavan
中科院分区：
文献类型：
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作者：
Saandeep Aathreya;Meghna Chaudhary;T. Neal;Shaun J. Canavan
我们提出了一个新的多模式、基于上下文的数据集，用于持续身份验证。该数据集包含 27 名受试者，年龄范围为 [8, 72]，其中数据是在受试者观看旨在引发情绪反应的视频时在多个会话中收集的。收集的数据包括加速度计数据、心率、皮肤电活动、皮肤温度和面部视频。我们还提出了一种在使用所提出的数据集时进行公平比较的基线方法。该方法结合使用预训练骨干网络和人脸监督对比损失。还从生理信号中提取时间序列特征，用于分类。这种方法在所提出的数据集上，电信号的平均准确度、精确度和召回率分别为 76.59%、88.90 和 53.25，人脸视频的平均准确度、精确度和召回率分别为 90.39%、98.77 和 75.71。
We present a new multimodal, context-based dataset for continuous authentication. The dataset contains 27 subjects, with an age range of [8, 72], where data has been collected across multiple sessions while the subjects are watching videos meant to elicit an emotional response. Collected data includes accelerometer data, heart rate, electrodermal activity, skin temperature, and face videos. We also propose a baseline approach for fair comparisons when using the proposed dataset. The approach uses a combination of a pretrained backbone network with supervised contrastive loss for face. Time-series features are also extracted, from the physiological signals, which are used for classification. This approach, on the proposed dataset, results in an average accuracy, precision, and recall of 76.59%, 88.90, and 53.25, respectively, on electrical signals, and 90.39%, 98.77, and 75.71, respectively on face videos.