Face Dataset
Face datasets are crucial for training and evaluating facial analysis systems, encompassing diverse applications from emotion recognition to security. Current research emphasizes creating more representative datasets, addressing biases related to demographics, lighting conditions, and image quality, often employing generative models like GANs and diffusion models to augment or synthesize data. This work is vital for improving the fairness, accuracy, and robustness of facial analysis technologies across various contexts, mitigating potential societal biases and enhancing the reliability of applications in diverse fields.
Papers
May 3, 2022
March 11, 2022
February 24, 2022