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JBE, vol. 28, no. 2, pp.238-241, March, 2023


Facial Age Classification and Synthesis using Feature Decomposition

Chanho Kim and In Kyu Park

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Recently deep learning models are widely used for various tasks such as facial recognition and face editing. Their training
process often involves a dataset with imbalanced age distribution. It is because some age groups (teenagers and middle age) are
more socially active and tends to have more data compared to the less socially active age groups (children and elderly). This
imbalanced age distribution may negatively impact the deep learning training process or the model performance when tested against
those age groups with less data. To this end, we propose an age-controllable face synthesis technique using a feature
decomposition to classify age from facial images which can be utilized to synthesize novel data to balance out the age distribution.
We perform extensive qualitative and quantitative evaluation on our proposed technique using the FFHQ dataset and we show that
our method has better performance than existing method.

Keyword: Face synthesis, feature decomposition, age classification

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