TL;DR
This study suggests that different preprocessing strategies can influence classification performance within deep learning-based pipelines for improved facial expression recognition.
A non-exclusive learning search-Fossa optimization algorithm integrated with a convolutional neural network (NELS-FOA-CNN) is proposed to select the most relevant features for accurate FER, demonstrating improved performance compared with the baseline GCN.
Human communication involves emotion as an important issue. Because of emotion, people express and understand feelings, attitudes, and actions. However, effective automatic emotion recognition is still difficult to achieve when based on one single cue, as for instance facial expressions or voice may both present weak o...
This study presents the development and application of a real-time interest level estimation system for interactive training settings and proposes a mapping between learning engagement measures and conventional Facial Expression Recognition (FER) categories.
Facial expression prediction has gained considerable attention in recent years, particularly because of its healthcare related applications in human-computer interaction. This paper presents a comparative evaluation of machine learning and deep learning models for the prediction of face emotion using the CK+ dataset pr...
Facial expression and micro-expression recognition have become essential research topics in affective computing, computer vision, and intelligent human–computer interaction. Despite recent advances, accurately recognizing subtle facial movements remains challenging because of their short duration, low intensity, and th...
In the context of affective computing, real-time facial analysis plays an important role in human–computer interaction and personalized applications. This study presents a controlled comparative evaluation of five widely used deep neural network architectures EfficientNetB0, VGG16, MobileNetV2, InceptionV3, and ResNet5...
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