Context-adaptive and user-centric facial emotion classification

In this paper, we proposed a context-adaptive and user-centric emotion classification scheme of low complexity. Different people express their feelings in a different way under different circumstances (different context). Therefore, an adaptable architecture is proposed in this paper able to automatically update its performance to a particular individual (user-centric) and context environment (context-adaptive). As a result, the same expressions may lead to different emotional states in accordance to the specific environment to these feelings are expressed. The adaptation is performed using concepts derived from functional analysis. The presented adaptable architecture requires low memory and processing capabilities and thus it can be embedded in smart pervasive devices of low processing requirements. Experimental results on real-life databases illustrate the efficiency of the proposed scheme in recognizing the emotion of different people or even the same under different circumstances.