  Image-based age estimation aims to predict a person's age from facial images.
It is used in a variety of real-world applications. Although end-to-end deep
models have achieved impressive results for age estimation on benchmark
datasets, their performance in-the-wild still leaves much room for improvement
due to the challenges caused by large variations in head pose, facial
expressions, and occlusions. To address this issue, we propose a simple yet
effective method to explicitly incorporate facial semantics into age
estimation, so that the model would learn to correctly focus on the most
informative facial components from unaligned facial images regardless of head
pose and non-rigid deformation. To this end, we design a face parsing-based
network to learn semantic information at different scales and a novel face
parsing attention module to leverage these semantic features for age
estimation. To evaluate our method on in-the-wild data, we also introduce a new
challenging large-scale benchmark called IMDB-Clean. This dataset is created by
semi-automatically cleaning the noisy IMDB-WIKI dataset using a constrained
clustering method. Through comprehensive experiment on IMDB-Clean and other
benchmark datasets, under both intra-dataset and cross-dataset evaluation
protocols, we show that our method consistently outperforms all existing age
estimation methods and achieves a new state-of-the-art performance. To the best
of our knowledge, our work presents the first attempt of leveraging face
parsing attention to achieve semantic-aware age estimation, which may be
inspiring to other high level facial analysis tasks. Code and data are
available on \url{https://github.com/ibug-group/fpage}.
