  Age and gender recognition in the wild is a highly challenging task: apart
from the variability of conditions, pose complexities, and varying image
quality, there are cases where the face is partially or completely occluded. We
present MiVOLO (Multi Input VOLO), a straightforward approach for age and
gender estimation using the latest vision transformer. Our method integrates
both tasks into a unified dual input/output model, leveraging not only facial
information but also person image data. This improves the generalization
ability of our model and enables it to deliver satisfactory results even when
the face is not visible in the image. To evaluate our proposed model, we
conduct experiments on four popular benchmarks and achieve state-of-the-art
performance, while demonstrating real-time processing capabilities.
Additionally, we introduce a novel benchmark based on images from the Open
Images Dataset. The ground truth annotations for this benchmark have been
meticulously generated by human annotators, resulting in high accuracy answers
due to the smart aggregation of votes. Furthermore, we compare our model's age
recognition performance with human-level accuracy and demonstrate that it
significantly outperforms humans across a majority of age ranges. Finally, we
grant public access to our models, along with the code for validation and
inference. In addition, we provide extra annotations for used datasets and
introduce our new benchmark.
