A coarse-to-fine framework with ordinal regression for facial age estimation

This study introduces a novel coarse-to-fine framework combined with ranking regression designed to capture the ordinal nature of age progression. Our approach initially categorizes ages into broader groups, utilizing the inherent order of age labels to refine age estimation hierarchically. A ranking regression model then meticulously fine-tunes the predictions, resulting in a more accurate age estimate. We present a multi-stage neural network architecture that first differentiates between broad age categories and then hones in on more specific age distinctions. Our evaluation of multiple benchmark datasets indicates a substantial reduction in prediction error over current leading models. The empirical findings highlight the effectiveness of our methodology in addressing the complex, non-linear patterns of facial aging. The proposed method propels the domain of age estimation forward and provides a versatile framework for other ordinal regression tasks.