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Top2. Emotion Recognition Approaches
There are various studies using different methods in emotion recognition domain. Several between them are based on speech processing, such as voice analysis using statistical methods (Mohanty et al., 2010) and those using spectral features such as Mel-frequency cepstral coefficients (MFCCs) as clearly explained by (Samal et al., 2013).
The second emotional recognition source is the neurological signals, like is developed by Kumar (Kumar et al., 2019), their system used an EEG to recognize emotions. Another source in emotion recognition is the facial expression which is the most natural and significant way to express emotions. Hai has proposed a model of facial expression classification using artificial neural network ANN and KNN (Hai et al., 2015). This model has a classifying accuracy equal to 92.38% but a significant amount of data has been used, for each face, six feature vectors: one global feature representing the whole face and five local feature vectors representing the eyebrow, eye and mouth of the face. (De et al., 2015) have modeled Eigen face approach to recognize the human facial expressions This method uses the Hue-Saturation-Value color model to detect the face in an image, they used a statistical approach has been used for reducing the high dimensionality of the Eigen space and then by projecting the test image upon the Eigen space and calculating the Euclidean distance between the test image and mean of the Eigen faces of the training dataset the expressions are classified. The principal disadvantages, those statistical methods are computationally expensive and complex with the increase in data size and time complexity is high (R. Khan & Sharif, 2017). (Yu & Liu, 2015) have combined the appearance descriptors and geometric features of the image for facial expression recognition. They have employed by orthogonal wavelet entropy to extract multi-scale features and used fuzzy multiclass support vector machine to be the classifier. Alhussein has used multi-scale Weber local descriptor (MS-WLD) and Support Vector Machine (SVM) (Alhussein, 2016). Chao has enhanced the performance of the popular Local binary patterns (LBP) feature. (Chao and Frank, 2015) concentrated on the recognition of units of facial action (AUs). They have adopted independent component analysis (ICA) as the extraction method and SVM as the model classifier. IN the field of emotion recognition, multilayer perceptron is widely used. Let's mention the work of (Danisman et al., 2013; Hayet et al., 2014; Palo et al., 2015).