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The proposed framework in this paper has the primary objective of classifyingthe facial expression shown by a person. These classifiable expressions can beany one of the six universal emotions along with the neutral emotion. After theinitial facial localization is performed, facial landmark detection and featureextraction are applied where in the landmarks are determined to be the fiducialfeatures: the eyebrows, eyes, nose and lips. This is primarily done usingstate-of-the-art facial landmark detection algorithms as well as traditionaledge and corner point detection methods using Sobel filters and Shi Tomasicorner point detection methods respectively. This leads to generation of inputfeature vectors being formulated using Euclidean distances and trained into aMulti-Layer Perceptron (MLP) neural network in order to classify the expressionbeing displayed. The results achieved have further dealt with higher uniformityin certain emotions and the inherently subjective nature of expression.

Facial Expression Recognition using Facial Landmark Detection and Feature Extraction via Neural Networks