Paper #2

Enhancing Feature Extraction Technique Through Spatial Deep Learning Model for Facial Emotion Detection

Nizamuddin Khan, Ajay Singh and Rajeev Agrawal

Abstract: Automatic facial expression analysis is a fascinating and difficult subject that has implications in a wide range of fields, including human–computer interaction and data-driven approaches. Based on face traits, a variety of techniques are employed to identify emotions. This article examines various recent explorations into automatic data-driven approaches and handcrafted approaches for recognising face emotions. These approaches offer computationally complex solutions that provide good accuracy when training and testing are conducted on the same datasets, but they perform less well on the most difficult realistic dataset, FER-2013. The article's goal is to present a robust model with lower computational complexity that can predict emotion classes more accurately than current methods and aid society in finding a realistic, all-encompassing solution for the facial expression system. A crucial step in good facial expression identification is extracting appropriate features from the face images. In this paper, we examine how well-known deep learning techniques perform when it comes to facial expression recognition and propose a convolutional neural network-based enhanced version of a spatial deep learning model for the most relevant feature extraction with less computational complexity. That gives a significant improvement on the most challenging dataset, FER-2013, which has the problems of occlusions, scale, and illumination variations, resulting in the best feature extraction and classification and maximizing the accuracy, i.e., 74.92%. It also maximizes the correct prediction of emotions at 99.47%, and 98.5% for a large number of samples on the CK+ and FERG datasets, respectively. It is capable of focusing on the major features of the face and achieving greater accuracy over previous fashions.

Keywords: Convolutional neural network; Facial expression recognition; Spatial deep learning; Spatial transform network.