How a face with South Asian ancestry scores across 17 structural metrics. Descriptive anthropometry, not a hierarchy.
Beauty is multi-ethnic. The published cross-cultural preference research is clear: no population scores higher than another in aggregate. This page describes the distribution, not a verdict.
17 metrics · Multi-ethnic norms · Free · No signup
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Legacy face-scoring tools were calibrated on European-dominant datasets. The nasal index, lip thickness, and palpebral fissure thresholds were set against one population's mean. When a wider nasal base, fuller lips, or a different palpebral angle from a South Asian face is scored against those thresholds, the metric reads as a deviation from a norm that was never meant to be universal.
The published South Asian craniofacial work (Kumari et al. on Indian norms; Ngeow and Aljunid 2009 on Malaysian and broader regional norms; Ferrario et al. on multi-ethnic norms) documents distributions that diverge meaningfully on a handful of specific metrics. A useful report shows both percentiles. The universal percentile answers where you sit across all populations; the population-appropriate percentile answers where you sit relative to faces with similar ancestry. Both are descriptive; the gap between them is itself diagnostic of where the universal norm is dominated by one population's mean.
The 17 metrics themselves are universal geometry. Facial thirds, fifths, FWHR, canthal tilt, jawline ratio, lip ratios, philtrum length, eye aspect ratio, brow-to-eye distance, nasal index, midface ratio, and the rest are defined the same way regardless of population. The reference distribution changes; the measurement does not.
Published South Asian nasal-width-to-height norms (Kumari et al. on Indian populations) sit in a distinct sub-range from European norms. The population-appropriate percentile prevents the European mean from being treated as a universal target.
Average upper and lower lip thickness sits higher than European norms. The published distributions place this as population mean rather than as a deviation, so the lip metric reads at average percentile against the appropriate reference.
Average canthal tilt sits in a distinct range. The eye aperture geometry registers slightly differently and the population-appropriate norm carries this as descriptive context.
Lower-face height relative to total facial height clusters in a distinct sub-range. Carried as descriptive percentile rather than as a deviation from European norms.
Denser brow hair and a typically lower hairline shift the brow-to-eye distance metric. The score treats this as structural signal rather than a styling artifact.
Skin tone is not a structural metric in the composite. What it affects is photo-quality requirements; the detector needs even, diffuse light to place landmarks accurately on darker skin, and a poorly lit photo will give a lower-confidence read regardless of underlying geometry.
Hiding the universal percentile would protect the user from an unflattering number but at the cost of the most honest read on a question some users genuinely want answered: where do I sit relative to faces in general. We show both. The universal percentile reflects the cross-population dataset; the population-appropriate percentile reflects the published distribution for similar ancestry. Use whichever is more useful for the question you brought to the page.
South Asia is itself a continuum. The South Asian-distribution norms aggregate North Indian, South Indian, Punjabi, Bengali, Sri Lankan, and many other populations with their own distinct craniofacial distributions. The population-appropriate percentile should be read as directional rather than as a precise read.
Free score is the headline. Population-appropriate context is the plan.
The $14.99 Looksmax Report scores all 17 metrics with both universal and South-Asian-distribution percentiles where validated norms exist, identifies your two weakest, and writes a soft-tissue-first plan.
Free, instant, private. 17 metrics with population-appropriate percentile context in the paid report.
17 metrics · Multi-ethnic norms · Photos auto-deleted
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