Abstract
Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods.
Highlights
Emotions play an important role in human daily life
Leila et al [4] proposed a global optimal feature fusion method for speech emotion recognition based on empirical mode decomposition and Teager-Kaiser Energy Operator (EMD-TKEO), according to the fact that the EMD combined with the TKEO gives an efficient time-frequency analysis of the non-stationary signals
The Augsburg Dataset and database for emotion analysis using physiological (DEAP) dataset were employed in order to fully verify the effectiveness of the proposed method
Summary
Emotions play an important role in human daily life. Reliable and accurate emotion recognition system is one of the key problems of achieving natural human-machine interaction (HMI) [2]. In the last few decades, a variety of approaches for detecting human emotion have been performed by using speech, facial expression and behavior (gesture/posture) or physiological signals [3]. Leila et al [4] proposed a global optimal feature fusion method for speech emotion recognition based on empirical mode decomposition and Teager-Kaiser Energy Operator (EMD-TKEO), according to the fact that the EMD combined with the TKEO gives an efficient time-frequency analysis of the non-stationary signals. In order to increase the accuracy rate of emotion recognition, unsupervised deep belief network (DBN) was proposed for depth level feature extraction from fused observations of Electro-Dermal
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