Real-time emotion recognition using end-to-end attention-based fusion network

Abstract

.Real-time emotion detection based on facial expression is an innovative research field that has been applied in several areas, such as health, human–machine vision, and autonomous safety. Researchers in object detection are involved in developing methods to interpret, code facial expressions, and extract these features to be better predicted by machines. Furthermore, the success of deep learning with different architectures is exploited to achieve better performance. But these methods drastically fail in excessive sweating in different health conditions. We aim to create a dataset in different health conditions and detect facial emotion using the encoder and decoder-based deep learning methodology. The proposed architecture and the dataset present the progress made by comparing the other proposed methods and the quantitative and qualitative results obtained. The major benefit of our study is to enhance the emotion detection efficiency with other proposed methods and real-time applications for different health conditions. We propose the application of feature extraction of facial expressions with an end-to-end attention module-based fusion network for detecting different facial emotions (happy, angry, neutral, surprised, etc.) with an accuracy of 99.68%. The proposed system depends upon the human face; as we know, the face reflects human brain activities or emotions.

Review and evaluation of recent advancements in image dehazing techniques for vision improvement and visualization

“…Incorporating attention mechanisms into dehazing models can prove highly beneficial since they enable the model to concentrate selectively on specific regions of the input image. Different approaches, such as spatial and channel attention, can be employed to integrate attention mechanisms into dehazing models to minimize the feature loss between encoder and decoder modules 81 – 83 …”

“…Different approaches, such as spatial and channel attention, can be employed to integrate attention mechanisms into dehazing models to minimize the feature loss between encoder and decoder modules. [81][82][83] An illustration of attention mechanism-based dehazing methods includes the AOD-Net 67 and AdaFM-Net. 12 The AOD-Net 67 adopts a multi-scale CNN architecture 64 that incorporates a spatial attention module.…”