Basic research on facial expression recognition model with adaptive learning capability

Most facial expression recognition models that have been proposed eventually create some classifier based on the expression images taken during a short period of time and using them as base data for learning. However, because so many facial expression patterns exist that a human being cannot make representations of all of them, it is difficult to obtain and retain all available patterns and use them as learning data in a short time. For a facial expression recognition model to retain its high robustness along the time axis continuously for a long time, the classifier created at the initial stage should be evolved to be adaptive gradually over time. In other words, what is necessary for the model is that it retains existing knowledge (i.e. past facial patterns) and simultaneously learns to keep adding newly available knowledge (i.e. new facial patterns) as it becomes available. As described in this paper, we propose a method of creating a facial expression recognition model that can offer the adaptive learning capability described above.