Age Estimation

Age estimation, the process of determining an individual's age from various data sources, aims to improve accuracy and efficiency across diverse applications. Current research focuses on developing robust models using deep learning architectures like convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers, often incorporating techniques such as contrastive learning, attention mechanisms, and multi-task learning to leverage diverse data modalities (e.g., facial images, MRI scans, speech). These advancements are impacting fields ranging from healthcare (brain age estimation, disease diagnosis) to security (age verification) and digital forensics, where accurate age estimation is crucial for effective investigation and legal proceedings. The field is also actively addressing challenges like data imbalance, handling ambiguous cases, and ensuring fairness and inclusivity in model performance.