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Published research highlights the presence of demographic bias in automated facial attribute classification algorithms, particularly impacting women and individuals with darker skin tones. Existing bias mitigation techniques typically require demographic annotations and often obtain a trade-off between fairness and accuracy, i.e., Pareto inefficiency. Facial attributes, whether common ones like gender or others such as "chubby" or "high cheekbones", exhibit high interclass similarity and intraclass variation across demographics leading to unequal accuracy. This requires the use of local and subtle cues using fine-grained analysis for differentiation. This paper proposes a novel approach to fair facial attribute classification by framing it as a fine-grained classification problem. Our approach effectively integrates both low-level local features (like edges and color) and high-level semantic features (like shapes and structures) through cross-layer mutual attention learning. Here, shallow to deep CNN layers function as experts, offering category predictions and attention regions. An exhaustive evaluation on facial attribute annotated datasets demonstrates that our FineFACE model improves accuracy by $$1.32\%$$ to $$1.74\%$$ and fairness by $$67\%$$ to $$83.6\%$$, over the SOTA bias mitigation techniques. Importantly, our approach obtains a Pareto-efficient balance between accuracy and fairness between demographic groups. In addition, our approach does not require demographic annotations and is applicable to diverse downstream classification tasks. To facilitate reproducibility, the code and dataset information is available at~\url{https://github.com/VCBSL-Fairness/FineFACE}.
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This content will become publicly available on December 6, 2025
Demographic bias mitigation at test-time using uncertainty estimation and human–machine partnership
Facial attribute classification algorithms frequently manifest demographic biases by obtaining differential performance across gender and racial groups. Existing bias mitigation techniques are mostly in-processing techniques, i.e., implemented during the classifier’s training stage, that often lack generalizability, require demographically annotated training sets, and exhibit a trade-off between fairness and classification accuracy. In this paper, we propose a technique to mitigate bias at the test time i.e., during the deployment stage, by harnessing prediction uncertainty and human–machine partnership. To this front, we propose to utilize those lowest percentages of test data samples identified as outliers with high prediction uncertainty. These identified uncertain samples at test-time are labeled by human analysts for decision rendering and for subsequently retraining the deep neural network in a continual learning framework. With minimal human involvement and through iterative refinement of the network with human guidance at test-time, we seek to enhance the accuracy as well as the fairness of the already deployed facial attribute classification algorithms. Extensive experiments are conducted on gender and smile attribute classification tasks using four publicly available datasets and with gender and race as the protected attributes. The obtained outcomes consistently demonstrate improved accuracy by up to 2% and 5% for the gender and smile attribute classification tasks, respectively, using our proposed approaches. Further, the demographic bias was significantly reduced, outperforming the State-of-the-Art (SOTA) bias mitigation and baseline techniques by up to 55% for both classification tasks.
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- Award ID(s):
- 2345561
- PAR ID:
- 10570118
- Editor(s):
- NA
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Machine learning with applications
- Edition / Version:
- 1
- Volume:
- 19
- Issue:
- 1
- ISSN:
- 2666-8270
- Page Range / eLocation ID:
- 1-30
- Subject(s) / Keyword(s):
- Facial attribute classifier Human–machine partnership Demographic bias mitigation Continual learning Test-time methods
- Format(s):
- Medium: X Size: 2 Other: pdf
- Size(s):
- 2
- Sponsoring Org:
- National Science Foundation
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