Abstract:
Facial expressions (FE) or human countenance reflect psychological reactions or
intentions stimulated within the mind in response to any social or personal event. These
expressions play a significant role in conveying messages to the observer in non-verbal
stealth mode. With the advancements in technology, facial expression recognition (FER)
is considered crucial in understanding human behavior. We can infer those feelings and
expressions are the essences of any interaction. In the same way, we need to make human
machine interaction as communal as human-human interaction by making machines
proficient at detecting human emotions by reading facial expressions. From recent year’s
discoveries, a Set of multiple features have been recognized that provide possibly useful
outcomes in the field of emotion recognition. Few preprocessing steps have been
performed on the image data set before the extraction of features. In this work different
prevalent methodologies, techniques and the types of features that are used by
researchers in the past to predict the facial expression over time will be combined, so that
a new and more efficient model can be designed. The purpose of this research is to design
an automated system which can recognize seven basic emotions of human namely anger,
disgust, fear, happy, sadness, Neutral and surprise for effective communication between
humans and computers. The single algorithm to provide perfect recognition in all the
scenarios has never been established so far; however, the research has been in progress
to develop substitutes or new models to improve the recognition process. A deep learning
algorithm is explained in this research work for classifying the facial expression of the
human. The proffered method investigates the effectiveness of deep convolution neural
network (DCNN) with the help of multiple models, and the best achieved result is 94.88%
of FER2013
