Facial Micro Emotion Detection and Classification Using Swarm Intelligence based Modified Convolutional Network

Abstract

Emotions are what makes us humans. Recognizing human emotions from facial micro expression features help us learn the true emotional state of a person. This technique of classifying the micro expressions can be used in varied application domains like criminology, marketing, job analysis, online learning etc. The field of recognizing micro emotions deals with tracking, recognizing, estimating & sequencing and classifying the recognized expressions. Artificial intelligence plays a crucial function in modern era of technology; micro expression analysis forms an ideal candidate of Deep Learning to correctly recognize these micro expressions when on display. The aim is to build a system that takes in a video data from any source and to recognize the micro expressions exhibited at various points in time. The challenge to overcome is to capture the fast changing expressions and to extract and align these facial features in order to extract suitable frames that provide the information from which the micro expressions can be ascertained by introducing it to a swarm optimization approach called the Artificial Bee Colony Approach. Implemented a novel approach that captures the essence of the micro expressions by an optical flow vector technique, that supplies its input to the modified deep learning Convolutional Neural Network, that in turn, is trained to categorize micro expressions on display. The Convolutional Neural Network combined with the Swarm Optimizer was able to achieve an accuracy of around 99.45% in identifying & classifying the facial micro expressions.