Abstract:
Humans are precise in facial expressions and recognizing them. Facial
expression is a routine task of humans. Training Computers to recognize such humanly
task as closed to humans can be useful in numerous ways such as offering services as
per the mode of consumer or user and will enhance the phenomenon of human computer
interaction. Environment and surroundings are the sources from where humans naturally
learn the models of facial expressions and they have logical models in brain, and with
the comparison of models, they are able to recognize the expressions of others. This
research aimed to design and develop a robust facial expression recognition system by
combining various techniques available in Computer Vision and Machine Learning.
Hence, it was crucial to understand the human psychological aspects of facial expression
in a precise manner. For the training and demonstration purposes, human volunteers were
utilized along with facial expression dataset such as CK+ from internet. Local Binary
Patterns (LBP) and Convolutional Neural Network (CNN) were occupied for feature
extraction and optimization respectively. In addition, different kinds of AI based tools
and techniques were employed to mimic the human ability of recognizing facial
expressions. The outcome presents a novel approach to facial expression recognition
with modified LBP and CNN. The outcome of the research suggests that further studies
and optimization would lead to commercially viable solutions such as products or
services as per the expression and emotions of the consumers. The researchers will
continue to optimize the outcome to supersede humans in recognizing facial expressions
in future
