South Asian facial features
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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The Indo-Aryan vs Dravidian problem: South Asia is not one craniofacial distribution
Kumari et al.'s work on North Indian samples and the parallel South Indian datasets document a measurable craniofacial split inside the South Asian category. North Indian, Punjabi, and Pakistani Indo-Aryan populations cluster around one set of nasal-index and midface-projection means; Tamil, Telugu, Kannada, and Sri Lankan Dravidian populations cluster around another. The within-region difference is large enough that a Mumbai user and a Chennai user scored against the same regional aggregate will see different gaps between their universal percentile and their sub-population percentile.
The lip and nasal metrics carry the biggest implications. South Asian-distribution lip-vermilion thickness norms place fuller upper and lower lips at the population mean rather than as a European-distribution deviation. South Asian-distribution nasal-index norms run wider than European baselines but narrower than the African-distribution mean Coetzee 2014 documented. Same geometry, different reference axis, different read.
Palpebral fissure angle is the metric most distinct to the South Asian category. The Ngeow and Aljunid 2009 Malaysian-population data plus Kumari's Indian samples both record a slightly steeper average canthal tilt than European norms, which on a generic European-default tool reads as a percentile bump that is actually just the population mean. The dual-percentile output is the only way to separate "your face is above the South Asian distribution mean on canthal tilt" from "the European norm happens to be lower than the South Asian norm on this metric."
5 structural patterns documented in South Asian craniofacial research
Nasal index
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.
Lip thickness and vermilion ratio
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.
Palpebral fissure angle
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 proportion
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.
Brow density and hairline position
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 undertone and tone variance
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.
Why the universal percentile is still worth showing
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.
What this score cannot honestly tell a South Asian user
- The South Asian regional aggregate hides the Indo-Aryan vs Dravidian split documented in Kumari and parallel South Indian datasets. If your ancestry is specifically Punjabi, Bengali, Tamil, Telugu, Sri Lankan, or Bangladeshi, treat the sub-population percentile as directional — the published Indian norms sample some of these groups more thoroughly than others.
- The Ngeow and Aljunid 2009 dataset is calibrated to Malaysian samples that include large Indian and Chinese diaspora populations; we draw on it for the Indian-diaspora components but it is not a substitute for native-country Bangladeshi or Sri Lankan norms where validated standalone datasets are thinner.
- South Asian skin tone variance (very fair Punjabi/Kashmiri skin through deep Tamil/South Indian skin) is not a metric in the composite, but it is a real input to detector confidence. Even, diffuse light is the prerequisite for accurate landmark placement across the full tonal range; a low-confidence read on poorly lit darker-skin photos is fixable with photography, not with a different scoring model.
- The recommendation layer deliberately leans on grooming and habit interventions that work across the South Asian skin and feature range (skin clarity, brow shaping, dental whitening that reads well against deeper melanin contrast, sleep, posture). We do not recommend surgical interventions targeting ethnic features.
- Cross-cultural attractiveness research (Cunningham 1995, Rhodes 2006, Coetzee 2014) finds no aggregate hierarchy ranking South Asian faces above or below other populations. Any tool that scores a South Asian face lower because it uses a European-default reference distribution is making a methodology error this page is designed to expose.
South Asian facial features FAQ
Does this page rank South Asian faces against other ethnicities?+
Which structural metrics tend to differ in South Asian faces?+
Are the percentiles compared to South Asian norms or universal norms?+
Does the test handle wider nasal base or fuller lips correctly?+
How does darker skin tone affect the score?+
Is my photo uploaded?+
What does the free score include and what does the $14.99 report add?+
Free score is the headline. Population-appropriate context is the plan.
Get all 17 metrics with dual-percentile context.
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.
Score your face now
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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