Few-shot learning for facial expression recognition: a comprehensive survey

Abstract

Facial expression recognition (FER) is utilized in various fields that analyze facial expressions. FER is attracting increasing attention for its role in improving the convenience in human life. It is widely applied in human–computer interaction tasks. However, recently, FER tasks have encountered certain data and training issues. To address these issues in FER, few-shot learning (FSL) has been researched as a new approach. In this paper, we focus on analyzing FER techniques based on FSL and consider the computational complexity and processing time in these models. FSL has been researched as it can solve the problems of training with few datasets and generalizing in a wild-environmental condition. Based on our analysis, we describe certain existing challenges in the use of FSL in FER systems and suggest research directions to resolve these issues. FER using FSL can be time efficient and reduce the complexity in many other real-time processing tasks and is an important area for further research.

Classification of First-Episode Psychosis with EEG Signals: ciSSA and Machine Learning Approach

“…The fundamental principle of this approach is that by bringing together these models, a more robust and accurate overall model can be created, thereby offering more reliable predictions and options compared to any individual model. The importance of aggregating the outcomes of many classifiers to reduce generalization errors has been emphasized [38,39]. Ensemble techniques have been shown to be particularly effective due to the existence of different inductive biases among various classifier types.…”

“…For instance, the study by Missonnier et al was performed with eighteen healthy individuals and 15 individuals with FEP using 20-channel EEG signals. For these two groups, event-related gamma (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45) and delta (0.5-4 Hz) oscillatory responses were evaluated in a visual n-back working memory task. Oscillation periodicity analyses were calculated to investigate the relationship between the psychiatric condition reflected by each frequency range and the working memory load.…”

An In-Depth Review of Deep Facial Expression Recognition: Obstacles, Utilizations, and Prospective Directions

“…Datasets can be neatly bifurcated into 'in the lab' and 'in the wild' categories. An 'in the lab' dataset typically furnishes pristine, high quality data [2] alongside meticulously structured and annotated emotional cues, often mapped through the Facial Action Code [5], a system tailored for controlled environments. Conversely, 'in the wild' datasets encapsulate spontaneous facial expressions captured across diverse environmental contexts [6].…”

“…Researchers in [1] have substantiated that the human countenance holds unparalleled adaptability in gauging engagement levels. Extensive exploration of FER has been undertaken within the realm of human psychology [2].…”