Abstract
In this paper, we present a graph-causal regularization (GCR) for robust facial age estimation. Existing label facial age estimation methods often suffer from overfitting and overconfidence issues due to limited data and domain bias. To address these challenges and leveraging the chronological correlation of age labels, we propose a dynamic graph learning method that enforces causal regularization to discover an attentive feature space while preserving age label dependencies. To mitigate domain bias and enhance aging details, our approach incorporates counterfactual attention and bilateral pooling fusion techniques. Consequently, the proposed GCR achieves reliable feature learning and accurate ordinal decision-making within a globally-tuned framework. Extensive experiments under widely-used protocols demonstrate the superior performance of GCR compared to state-of-the-art approaches.
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Notes
- 1.
GCR outperformed other methods except for DLDL v2 and AVDL in Morph II setting I. However, they were pretrained on large dataset (i.e. IMDB-WIKI [24]) or private dataset.
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Acknowledgment
This work was supported in part by the National Science Foundation of China under Grants 62076142 and 62241603, in part by the National Key Research and Development Program of Ningxia under Grant 2023AAC05009, 2022BEG03158 and 2021BEB0406.
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Wang, T., Dong, X., Li, Z., Liu, H. (2024). Co-regularized Facial Age Estimation with Graph-Causal Learning. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14432. Springer, Singapore. https://doi.org/10.1007/978-981-99-8543-2_13
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