Facial Expression Recognition using Facial Landmark Detection and Feature Extraction on Neural Networks
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The proposed framework in this paper has the primary objective of classifying the facial expression shown by a person using facial landmark detection and feature extraction. These classifiable expressions can be any one of the six universal emotions along with the neutral emotion. After initial facial detection, facial landmark detection and feature extraction are performed (where in the landmarks were determined to be the fiducial features: the eyebrows, eyes, nose and lips). This is primarily done using the Sobel horizontal edge detection method and the Shi Tomasi corner point detector. This leads to input feature vectors being formulated and trained into a Multi-Layer Perceptron (MLP) neural network in order to classify the expression being displayed. Facial Expression Recognition (FER) is a significant step in reaching the eventual goal of artificial intelligence. If efficient methods can be brought about to automatically recognize these expressions, major advances may be achieved in computer vision.
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