As an important part of emotion research, facial expression recognition is a necessary\nrequirement in humanâ??machine interface. Generally, a face expression recognition system includes\nface detection, feature extraction, and feature classification. Although great success has been made\nby the traditional machine learning methods, most of them have complex computational problems\nand lack the ability to extract comprehensive and abstract features. Deep learning-based methods can\nrealize a higher recognition rate for facial expressions, but a large number of training samples and\ntuning parameters are needed, and the hardware requirement is very high. For the above problems,\nthis paper proposes a method combining features that extracted by the convolutional neural network\n(CNN) with the C4.5 classifier to recognize facial expressions, which not only can address the\nincompleteness of handcrafted features but also can avoid the high hardware configuration in the\ndeep learning model. Considering some problems of overfitting and weak generalization ability\nof the single classifier, random forest is applied in this paper. Meanwhile, this paper makes some\nimprovements for C4.5 classifier and the traditional random forest in the process of experiments.\nA large number of experiments have proved the effectiveness and feasibility of the proposed method.
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