Xception transfer learning with early stopping for facial age estimation
Marina V. Polyakova,
Vladyslav V. Rogachko,
Oleksandr H. Nesteriuk
et al.
Abstract:The rapid development of deep learning attracts more attention to the analysis of person's face images. Deep learning methodsof facial age estimation are more effective compared to methods based on anthropometric models, models of active appearance, texture models, subspace of aging patterns. However, deep learning networks require more computing power to process images. Pre-trained models do not need a large training set and their training time is less. However, the parameters obtained as a re… Show more
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