User cooperative behaviour is mandatory and valuable to warranty data acquisition quality in forensic biometrics. In the present paper, we consider human cooperative behaviour in front of wearable security cameras. Moreover, we propose a human cooperation detection pipeline based on deep learning. Recently, recurrent neural networks (RNN) have shown remarkable performance on several tasks such as image captioning, video analysis, or natural language processing. Our proposal describes an RNN architecture with the aim at detecting whether a human is exhibiting an adversarial behaviour by trying to avoid the camera. This data is obtained by analysing the noise patterns of human movement. More specifically, we are not only providing an extensive analysis on the proposed pipeline considering different configurations and a wide variety of RNN types, but also an ensemble of the generated models to outperform each single model. The experiment has been carried out using videos captured from a mobile device camera (GOTCHA Dataset) and the obtained results have demonstrated the robustness of the proposed method.

用户合作行为是强制性的，对于法医生物识别中的保修数据获取质量非常重要。在本文中，我们考虑了可穿戴式安全摄像机前面的人类合作行为。此外，我们提出了基于深度学习的人员合作检测管道。最近，递归神经网络（RNN）在多项任务（例如图像字幕，视频分析或自然语言处理）上表现出了卓越的性能。我们的提案描述了一种RNN架构，旨在通过尝试避开摄像头来检测人类是否表现出对抗性行为。该数据是通过分析人体运动的噪声模式获得的。更具体地说，我们不仅会针对建议的管道提供广泛的分析，同时考虑不同的配置和多种RNN类型，而且还可以将生成的模型合为一体，以胜过每个单个模型。使用从移动设备相机（GOTCHA数据集）捕获的视频进行了实验，获得的结果证明了所提出方法的鲁棒性。