Extreme learning machine (ELM) play a significant role in pattern recognition, and pattern classification applications. This employs a feed-forward neural network having only one hidden layer unlike conventional neural network architecture. Due to the simple and layered architecture, this classification computationally fast compared to other machine learning algorithms. Two kernel functions are used in the output layer such as Radial Basis Function (RBF) and Multi-Layer Perceptron (MLP) to classify the emotions. We investigated four different activation functions (tanh, sigmoid, Gaussian, and hardlim) in the MLP kernel for evaluation of comparative performance. The grid search method is used to determine the optimal value of RBF width in the ranges of 0.01 to 0.1 with a step value of 0.01 and the hidden neurons of 1000—2500 with a step value of 100.

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