An Occlusion Aware Facial Expression Recognition Model Using Fitness Based Cheetah Optimizer and Adaptive Multi�Scale ViT-CNN With Attention Mechanism

As a highly nuanced aspect of human communication, facial
expression recognition presents a computationally complex problem, making
it a prominent area of research in computer vision and affective computing.
Problems like poor image quality, occlusions, inconsistent illumination, and
head attitude changes are frequently observed in images taken from
unstructured sources such as the internet that affect the accuracy of facial
expression performance. With the aim of resolving these issues, an
innovative occluded Facial Expression Recognition (FER) using an
advanced deep learning model is proposed. For recognizing facial
expressions, images are gathered in benchmark sources. The Viola-Jones
(VJ) facial detector model is processed using the collected images. The
detected face images from the VJ are given to the Regions of Interest (ROI)
extraction process. The extracted ROI is passed to the Adaptive and
Multiscale Vision Transformer-Convolutional Neural Network with
Attention Mechanism (AMViTCNN-AM) for recognizing facial
expressions. AMViTCNN-AM accurately identifies the expression in the
face images even in the presence of occlusion. To get better performance in
the FER process, the parameters in the network are optimized by the Fitness�based Cheetah Optimizer (F-CO). Experiments are carried out to prove the
efficiency of the designed framework. The outcomes show that the
implemented approach attained an accuracy value of 98.43%, which proves
the potential of a developed deep learning model in the FER.