Abstract: For the affective recognition from pulse signal, a new approach was presented, which combined correlation analysis and max-min ant colony algorithm. The effective feature subset which can identify the affective recognition model with better performance was found. Firstly, sequential backward sclection(SBS) was used for sorting the original fcalures. Secondly, the linear correlation coefficient was presented for calculating the correlation between features, and some features were removed which had greater correlation according to the result of sorting. Finally, max-min ant colony algorithm realized feature selection which searched for an optimal subset based on the compact feature subset, and combined with Fisher classifier to finish classification of six emotions which include happiness, surprise, disgust, grief, anger and fear. The experiments show that the proposed approach can find the more stable and effective feature subset from the original feature set,and establish effective affective recognition model.

Key words: Affective recognition, Pulse signal, Correlation analysis, Max-min ant colony algorithm