pith:3QPZ3RBF
Auditing Demographic Bias in Facial Landmark Detection for Fair Human-Robot Interaction
After controlling for head pose and image resolution, facial landmark detectors show no bias by gender or race but retain an age-related bias.
arxiv:2604.06961 v2 · 2026-04-08 · cs.CV
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\pithnumber{3QPZ3RBFEMGESGKQ6EAAQJ4ZJP}
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Claims
Notably, after accounting for these confounders, we show that performance disparities across gender and race vanish. However, we identify a statistically significant age-related effect, with higher biases observed for older individuals.
The controlled statistical methodology successfully disentangles demographic effects from confounding visual factors such as head pose and image resolution without residual interactions or dataset-specific artifacts that could mask or create apparent biases.
After accounting for confounding factors like head pose and resolution, demographic biases in facial landmark detection largely vanish except for a statistically significant age effect disadvantaging older people.
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| First computed | 2026-06-11T01:10:35.796627Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
dc1f9dc425230c491950f1000827994bdd3388dfd07e11e773ae934291741c17
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/3QPZ3RBFEMGESGKQ6EAAQJ4ZJP \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: dc1f9dc425230c491950f1000827994bdd3388dfd07e11e773ae934291741c17
Canonical record JSON
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