Hierarchical Attention-Based Age Estimation and Bias Analysis
In this work, we present a Deep Learning approach to estimate age from facial images. First, we introduce a novel attention-based approach to image augmentation-aggregation, which allows multiple image augmentations to be adaptively aggregated using a Transformer-Encoder. A hierarchical probabilisti...
Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 45(2023), 12 vom: 26. Dez., Seite 14682-14692 |
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Format: | Online-Aufsatz |
Sprache: | English |
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2023
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Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
Schlagworte: | Journal Article |
Zusammenfassung: | In this work, we present a Deep Learning approach to estimate age from facial images. First, we introduce a novel attention-based approach to image augmentation-aggregation, which allows multiple image augmentations to be adaptively aggregated using a Transformer-Encoder. A hierarchical probabilistic regression model is then proposed that combines discrete probabilistic age estimates with an ensemble of regressors. Each regressor is adapted and trained to refine the probability estimate over a given age range. We show that our age estimation scheme outperforms current schemes and provides a new state-of-the-art age estimation accuracy when applied to the MORPH II and CACD datasets. We also present an analysis of the biases in the results of the state-of-the-art age estimates |
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Beschreibung: | Date Revised 07.11.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
ISSN: | 1939-3539 |
DOI: | 10.1109/TPAMI.2023.3319472 |