MEDNET – an approach to facial micro-emotion recognition using pixel binning and local Binary pattern - convolutional neural network

Abstract

Facial-Expression recognition is a very intriguing field of research, due to the complexity in its approach and applicability of widely available databases. However, Micro-expression recognition is quite a vague yet growing area of research due to its applicability in revealing minute facial expressions. These emotional triggers happen only under very pressing circumstances, which means detecting them can also be extremely tough due to shortage of time during which it lasts. In this study, the approach to Micro-facial expression detection is to explore passive and real-time observation that produces a great result for micro-facial expression recognition using a vast data set trained using new training techniques. A total of 59 papers were analyzed whose concepts were associative to our main thesis concept, which were categorized into three stages: Construction of a new dataset which constituted of standard and new facial images, which was trained using innovative image processing pipelines, implementation of a new Binary Pattern layer our Neural Network layer to accelerate the models expression tracking abilities, creation of a new facial model capable of facial and micro-facial expression recognition that performs better statistically when compared to its counterparts. Furthermore, the new model was tested in both artificial and real-world scenarios to accentuate the reliability of the data sources.