Abstract:

Developing a machine's ability to recognize emotion states is one of the hallmarks of emotional intelligence and important prerequisite for high-level human computer interaction（HCI）.Recording and recognizing physiological signals of emotion has become an increasingly important field of research in affective computing and HCI.For the problem of feature redundancy of physiological signals-based emotion recognition and low efficiency of traditional feature reduction algorithms on great sample data,a hybrid intelligent optimization algorithm based on the simulated annealing algorithm and particle swarm optimization algorithm（SA-PSO）was proposed to solve the problem of emotion feature selection.Then a weighted discrete-KNN classifier（WD-KNN）was presented to classify features by making full use of emotion sample information.The recognition rate and efficiency was increased and the algorithm's validity was verified through the analysis of experimental simulation data and the comparison of several recognition methods.

Key words: affective computing, emotion recognition, feature selection, hybrid intelligent optimization