Adaptive feature selection-based AdaBoost-KNN with direct optimization for dynamic emotion recognition in human-robot interaction

AdaBoost-KNN using adaptive feature selection with direct optimization is proposed for dynamic emotion recognition in human-robot interaction, where the real-time dynamic emotion is recognized based on facial expression. It can make robots capable of understanding human dynamic emotions, in such a way that human-robot interaction is realized in a smooth manner. Based on the facial key points extracted by Candide-3 model, adaptive feature selection is adopted, namely Plus-L Minus-R Selection is completed. It can determine the features that contribute the most to emotion recognition, thereby forming the basis of emotion classification. Emotion classification is based on AdaBoost-KNN, which builds a series of KNN classifiers. AdaBoost-KNN adjusts the weights of the data in an iterative manner. Moreover, global optimal parameters are approximated with direct optimization until the recognition rate reaches its maximal value. The experimental performance of the proposal is verified by a $k$-fold cross-validation. Results show that the recognition rate of the proposed approach is higher than that of the AdaBoost-KNN, adaptive feature selection-based AdaBoost-KNN, and AdaBoost-KNN with direct optimization. It is also higher than the rate achieved by other traditional recognition methods, such as AdaBoost, KNN, and SVM. In addition, preliminary application experiments are developed in our emotional social robot system, composed of two mobile robots. The experiments demonstrate the dynamic emotion understanding ability of robots in human-robot interaction.