Web-based visualisation of head pose and facial expressions changes: monitoring human activity using depth data
This work addresses the need for comprehensible visual representations in human activity monitoring, with applications in serious gaming and human-computer interaction, though it is incremental as it builds on existing methods for head pose estimation and facial expression recognition.
The paper tackled the challenge of monitoring human activity by developing a system that visualizes head pose and facial expression changes using depth data from a Kinect sensor, achieving rapid and accurate head pose estimation in unconstrained environments and recognizing four universal facial expressions.
Despite significant recent advances in the field of head pose estimation and facial expression recognition, raising the cognitive level when analysing human activity presents serious challenges to current concepts. Motivated by the need of generating comprehensible visual representations from different sets of data, we introduce a system capable of monitoring human activity through head pose and facial expression changes, utilising an affordable 3D sensing technology (Microsoft Kinect sensor). An approach build on discriminative random regression forests was selected in order to rapidly and accurately estimate head pose changes in unconstrained environment. In order to complete the secondary process of recognising four universal dominant facial expressions (happiness, anger, sadness and surprise), emotion recognition via facial expressions (ERFE) was adopted. After that, a lightweight data exchange format (JavaScript Object Notation-JSON) is employed, in order to manipulate the data extracted from the two aforementioned settings. Such mechanism can yield a platform for objective and effortless assessment of human activity within the context of serious gaming and human-computer interaction.