An attention recurrent model for human cooperation detection

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.