Facial Expression Recognition using Facial Landmark Detection and Feature Extraction via Neural Networks
This work addresses facial expression recognition for applications like human-computer interaction, but it is incremental as it combines existing methods without major breakthroughs.
The paper tackles facial expression recognition by using facial landmark detection and feature extraction to classify six universal emotions plus neutral, achieving results that address uniformity in certain emotions and the subjective nature of expression.
The proposed framework in this paper has the primary objective of classifying the facial expression shown by a person. These classifiable expressions can be any one of the six universal emotions along with the neutral emotion. After the initial facial localization is performed, facial landmark detection and feature extraction are applied where in the landmarks are determined to be the fiducial features: the eyebrows, eyes, nose and lips. This is primarily done using state-of-the-art facial landmark detection algorithms as well as traditional edge and corner point detection methods using Sobel filters and Shi Tomasi corner point detection methods respectively. This leads to generation of input feature vectors being formulated using Euclidean distances and trained into a Multi-Layer Perceptron (MLP) neural network in order to classify the expression being displayed. The results achieved have further dealt with higher uniformity in certain emotions and the inherently subjective nature of expression.