Age Invariant Face Recognition

Age-invariant face recognition (AIFR) aims to develop systems that accurately identify individuals regardless of age-related changes in facial appearance. Current research focuses on disentangling age and identity features within facial images using techniques like mutual information minimization, contrastive learning, and multi-task learning frameworks often incorporating deep convolutional neural networks. These advancements are driven by the need for robust and reliable facial recognition across diverse age groups in applications such as security, law enforcement, and missing person identification, particularly addressing challenges posed by limited datasets and ethnic biases in existing models. The development of synthetic aging data and improved algorithms are key areas of ongoing investigation to enhance AIFR accuracy and generalizability.