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This paper presents a lightweight algorithm for feature extraction,classification of seven different emotions, and facial expression recognitionin a real-time manner based on static images of the human face. In this regard,a Multi-Layer Perceptron (MLP) neural network is trained based on the foregoingalgorithm. In order to classify human faces, first, some pre-processing isapplied to the input image, which can localize and cut out faces from it. Inthe next step, a facial landmark detection library is used, which can detectthe landmarks of each face. Then, the human face is split into upper and lowerfaces, which enables the extraction of the desired features from each part. Inthe proposed model, both geometric and texture-based feature types are takeninto account. After the feature extraction phase, a normalized vector offeatures is created. A 3-layer MLP is trained using these feature vectors,leading to 96% accuracy on the test set.

Real-Time Facial Expression Recognition using Facial Landmarks and Neural Networks