AGMixer: Age Estimation Using Gender Feature and Improved Ordinal Loss

Age estimation has long been a crucial topic in image processing, with applications across various scenarios. Since the aging process and the aging rate for men and women are different, in this work, we propose AGMixer, which leverages the gender information to reduce the error of age estimation. We use facial representation learning (FaRL) pretrained by the vision transformer (ViT) as a feature extractor and employ a mixer layer for effective feature fusion, achieving lower error rates in age estimation. To well utilize gender features and ordinal information in age, we annotated gender labels for CACD2000, CLAP2016, and FG-NET and improved the ordinal distance encoded regularization (ORDER) loss. We compared our method with others on UTKFace, AFAD, AgeDB, CACD2000, CLAP2016, and FG-NET datasets. Experiments show that the proposed algorithm achieves the lowest mean absolute error (MAE) across ALL of the 6 datasets. We make the source code public on GitHub.