Age Datasets

Age datasets are collections of data used to train and evaluate algorithms that estimate age from various sources, including images (faces, general photos, medical scans), sensor data (from devices or batteries), and even behavioral interactions (children with computers). Current research focuses on improving the accuracy and robustness of age estimation across diverse datasets, often employing deep learning models like convolutional neural networks (CNNs) and exploring techniques like contrastive learning and meta-learning to address challenges such as data imbalance and individual variability in aging. These advancements have implications for various fields, including forensic science, biometrics, healthcare (e.g., battery life prediction, disease progression monitoring), and developmental psychology.