Child Face

Research on child faces focuses on improving the accuracy and reliability of child face recognition systems, addressing challenges posed by age-related variations and biases in existing algorithms. Current efforts leverage techniques like generative adversarial networks (GANs) to create large, diverse datasets for training and evaluating these systems, and explore alternative biometric methods such as palmprint recognition for improved identification. These advancements are crucial for applications ranging from assisting humanitarian aid distribution to improving law enforcement capabilities, while also highlighting the need to mitigate biases in facial recognition technology.