Four-layer Convnet to Facial Emotion Recognition With Minimal Epochs and the Significance of Data Diversity

Abstract

Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communi- cation and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities

“…Whereas DL artificial neural networks can only learn after processing millions of data points, ML versus neural networks is better suited for business scenarios that can collect thousands of data points for the training datasets. Yet, ML maintenance calls for a group of specialists who can manually select features, categorize them, and modify algorithms when they stray [93] . DL involves substantially less human input and is capable of self-correcting when high forecast accuracy is at stake.…”