Table 5 A comparison of the provided methods in other papers and the proposed method for Emotion Recognition
Authors | Year | Method | Classification accuracy (%) |
|---|---|---|---|
Fan and Chou [66] | 2018 | Recurrence quantification analysis, logistic regression | 75.7% |
Zhong et al. [33] | 2017 | Spectral and time features, multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) | 77.19% (arousal accuracy), 76.17% (valence accuracy) |
Atkinson and Campos [22] | 2016 | Statistical and spectral features, Hjorth parameters, fractal dimension, minimum-Redundancy-Maximum-Relevance, support vector machine | 62.39% (valence), 60.72% (arousal) |
Xu and Plataniotis [32] | 2016 | Power spectral density, stacked denoising autoencoders, deep belief network | 85.86% (arousal accuracy of SDAE), 84.77% (valence accuracy of SDAE), 88.33% (arousal accuracy of DBN), 88.59% (valence accuracy of DBN) |
Jie et al. [67] | 2014 | Sample entropy, support vector machine | 79.11% |
Yin et al. [33] | 2017 | Spectral and time features, multiple-fusion-layer based ensemble classifier of stacked autoencoder | 77.19% (arousal accuracy) 76.17% (valence accuracy) |
Tripathi et al. [21] | 2017 | Convolutional neural networks, deep neural network | 58.44% (valence, DNN), 55.70% (arousal, DNN), 66.79% (valence, CNN), 57.58% (arousal, CNN) |
Alam et al. [29] | 2016 | Convolutional neural networks | 81.17% |
Kumar et al. [25] | 2016 | Bispectrum, least square support vector machine, radial basis function, linear neural network | 64.86% (arousal), 61.17% (valence) |
Our work | 2018 | The proposed method | 90.54% |