A Review on Facial Emotion Recognition and Classification Analysis with Deep Learning

Abstract

Automatic face expression recognition is an exigent research subject and a challenge in computer vision. It is an interdisciplinary domain standing at the crossing of behavioural science, psychology, neurology, and artificial intelligence. Human-robot interaction is getting more significant with the automation of every field, like treating autistic patients, child therapy, babysitting, etc. In all the cases robots need to understand the present state of mind for better decision making. It is difficult for machine learning techniques to recognize the expressions of people since there will be significant changes in the way of their expressions. The emotions expressed through the human face have its importance in making arguments and decisions on different subjects. Machine Learning with Computer Vision and Deep Learning can be used to recognize facial expressions from the preloaded or real time images with human faces. DNN (Deep Neural Networking) is one among the hottest areas of research and is found to be very effective in classification of images with a high degree of accuracy. In the proposed work, the popular dataset CK+ is analysed for comparison. The dataset FER 2013 and home-brewed data sets are used in the work for calculating the accuracy of the model created. The results are obtained in such a way that DCNN approach is very efficient in facial emotion recognition. Experiments and study show that the dataset, FER 2013 is a high-quality dataset with equal efficiency as the other two popular datasets. This paper aims to ameliorate the accuracy of classification of facial emotion.

A Flexible Semi-Synthetic Data Generator for Risky Drinking Behavior

“…With the ability to process a large number of complex datasets at a fast pace, machine learning (ML) has recently become a reliable and critical instrument for data analysis and has permeated a wide range of domains [13][14][15][16] , including natural language processing 17 , digital safety 18 , games 19 , and nance 20 . Likewise, excitement and attention have also been drawn into the eld of behavioral sciences, aiming to identify causal underpinnings that govern human behaviors, which can be performed through facial expression recognition 21,22 , speech classi cation 23,24 , and electroencephalogram screening 25,26 . In a recent study, by training on more than 45,000 data, Kim et al used ML to determine the leading factors to participants' drinking problem, which later helped facilitate a proper treatment plan 27 .…”