Facial expression recognition (FER) is significantly influenced by the cultural background (CB) of observers and the masking conditions of the target face. This study aimed to clarify these factors' impact on FER, particularly in machine-learning dat...
Facial expression recognition(FER) is a hot topic in computer vision, especially as deep learning based methods are gaining traction in this field. However, traditional convolutional neural networks (CNN) ignore the relative position relationship of ...
Real-time security surveillance and identity matching using face detection and recognition are central research areas within computer vision. The classical facial detection techniques include Haar-like, MTCNN, AdaBoost, and others. These techniques e...
Mathematical biosciences and engineering : MBE
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In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a b...
AIM: To build a facial image database and to explore the diagnostic efficacy and influencing factors of the artificial intelligence-based facial recognition (AI-FR) system for multiple endocrine and metabolic syndromes.
Individual identification plays a pivotal role in ecology and ethology, notably as a tool for complex social structures understanding. However, traditional identification methods often involve invasive physical tags and can prove both disruptive for ...
Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limi...
Facial inference, a cornerstone of person perception, has traditionally been studied through human judgments about personality traits and abilities based on people's faces. Recent advances in artificial intelligence (AI) have introduced new dimension...
BACKGROUND: Social Anxiety Disorder is traditionally diagnosed using subjective scales that may lack accuracy. Recently, EEG technology has gained importance for anxiety detection due to its ability to capture stable and objective neurophysiological ...