Abstract
Focusing on emotion recognition, this paper addresses the task of emotion classification and its performance with respect to accuracy, by investigating the capabilities of a distributed ensemble model using precision-based weighted blending. Research on emotion recognition and classification refers to the detection of an individual’s emotional state by considering various types of data as input features, such as textual data, facial expressions, vocal, gesture and physiological signal recognition, electrocardiogram (ECG) and electrodermography (EDG)/galvanic skin response (GSR). The extraction of effective emotional features from different types of input data, as well as the analysis of large volume of real-time data, have become increasingly important tasks in order to perform accurate classification. Taking into consideration the volume and variety of the examined problem, a machine learning model that works in a distributed manner is essential. In this direction, we propose a precision-based weighted blending distributed ensemble model for emotion classification. The suggested ensemble model can work well in a distributed manner using the concepts of Spark’s resilient distributed datasets, which provide quick in-memory processing capabilities and also perform iterative computations effectively. Regarding model validation set, weights are assigned to different classifiers in the ensemble model, based on their precision value. Each weight determines the importance of the respective classifier in terms of its performing prediction, while a new model is built upon the derived weights. The produced model performs the task of final prediction on the test dataset. The results disclose that the proposed ensemble model is sufficiently accurate in differentiating between primary emotions (such as sadness, fear, and anger) and secondary emotions. The suggested ensemble model achieved accuracy of 76.2%, 99.4%, and 99.6% on the FER-2013, CK+, and FERG-DB datasets, respectively.
Highlights
Introduction iationsEmotions have a vital role in the development of human–computer interaction and humanoid robots
The classification error is higher for classes such as disgust and surprise, because they have fewer samples
The classification error is higher for classes such as “fear”, because it has fewer samples
Summary
Emotion is among the most difficult things to define in psychology. There are several different definitions of emotions in the scientific literature. Corchs et al [26] look at the role of ensemble learning in emotion classification from both visual and linguistic perspectives They employed five state-of-the-art classifiers as independent models: naive Bayes (NB), Bayesian network (BN), closest nearest-neighbor (NN), decision tree (DT) and linear support vector machine (SVM). A study of lowlevel and mid-level features in convolutional neural networks (CNNs) for facial expression identification was undertaken by Nguyen et al [27] They suggested a model that included three different forms of mid-level links as part of an ensemble. The results revealed that the ensemble model can achieve a final classification accuracy of 74.09% in facial representation They used a method for face emotion identification that concatenated feature vectors from three multi-level networks, followed by fully connected layers.
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