The role of affective computing for ensuring safety in at-risk educational environments

This paper presents an introduction to the use of affective computing in at-risk educational environments, such as those that schools located in areas where there is armed conflict and for the safeguarding of children and at-risk adults more generally. This paper has discussed the improvement of the EigenFace based facial emotion recognition by continually streamlining the facial dataset used and its application in at-risk educational environments. One of these mimics the authors' EigenFaces library and appears to have better performance in poor lighting and poor camera situations, making it possibly better for drone use. It is therefore paramount that as the authors develop the system further that they keep each component separate, in case it is decided to utilise commercial libraries (e.g. Microsoft Project Oxford) for certain aspects. A structure such as VoisOver that allows for third party technologies to be plugged in would mean the authors' code can remain separate from that of third party plug-ins, namely specific algorithms for identifying the core 12 emotion sets the authors have devised in contexts that might not even have been considered yet. Such algorithms could work with the system described in this paper to make its operation in at-risk educational environments even more possible.