FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild

21 Jun 2021  ·  Yiming Lin, Jie Shen, Yujiang Wang, Maja Pantic ·

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}.

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Datasets


Introduced in the Paper:

IMDB-Clean

Used in the Paper:

MORPH CACD FG-NET KANFace

Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Age Estimation IMDB-Clean FP-Age Average mean absolute error 4.68 # 1
Age Estimation KANFace FP-Age Average mean absolute error 6.81 # 1

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