New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review

Abstract

Facial emotion recognition (FER) is a computer vision process aimed at detecting and classifying human emotional expressions. FER systems are currently used in a vast range of applications from areas such as education, healthcare, or public safety; therefore, detection and recognition accuracies are very important. Similar to any computer vision task based on image analyses, FER solutions are also suitable for integration with artificial intelligence solutions represented by different neural network varieties, especially deep neural networks that have shown great potential in the last years due to their feature extraction capabilities and computational efficiency over large datasets. In this context, this paper reviews the latest developments in the FER area, with a focus on recent neural network models that implement specific facial image analysis algorithms to detect and recognize facial emotions. This paper’s scope is to present from historical and conceptual perspectives the evolution of the neural network architectures that proved significant results in the FER area. This paper endorses convolutional neural network (CNN)-based architectures against other neural network architectures, such as recurrent neural networks or generative adversarial networks, highlighting the key elements and performance of each architecture, and the advantages and limitations of the proposed models in the analyzed papers. Additionally, this paper presents the available datasets that are currently used for emotion recognition from facial expressions and micro-expressions. The usage of FER systems is also highlighted in various domains such as healthcare, education, security, or social IoT. Finally, open issues and future possible developments in the FER area are identified.

Innovative Strategies for Early Autism Diagnosis: Active Learning and Domain Adaptation Optimization

“…For the binary classification, where there are only two classes, 0 and 1, the model’s prediction is given as follows [ 11 ]: …”

“…These datasets offer compelling advantages, are readily available, and are primed for deep learning algorithms. Moreover, facial images possess inherent characteristics that make them efficient training tools for applications like facial recognition and emotion analysis, ultimately aiding in developing accurate ASD diagnostic models [ 11 ].…”

Facial Expression Recognition for Measuring Jurors’ Attention in Acoustic Jury Tests

“…To combat these limitations, employing multiple observation channels and improving illumination conditions are suggested [42]. Secondly, context interpretation is another important aspect of understanding human emotions, and this can be a difficult task to accomplish for artificial intelligence-based systems [43]. Recognizing micro-expressions, which are brief and often involuntary facial movements, presents a significant challenge as they require precise motion tracking and recognition algorithms [44,45].…”

“…Figure 1 shows the map of the 68 landmarks detected through the Dlib face landmark predictor model. In particular, the distances between five pairs of points (i.e., 2-31, 16-31, 28-31, 37-40, [43][44][45][46] were considered to estimate the head orientation to the screen (Figure 1). Based on the usual symmetric characteristic of a face, such distances should remain constant between the left and right sides.…”

Exploring Pragmatics: Uncovering the Layers of Language and Meaning

“…In addition to being a useful tool for conveying information, language may also be used effectively to communicate emotions (Gross, 2015). The words we use, the way we compose phrases and even the tone of our voice may all give away our emotional state (Cîrneanu et al, 2023). Imagine receiving an email with the Subject in capital letters: IMPORTANT MEETING NOTICE -ACTION REQUIRED.…”