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 one South Asian regional aggregate would mis-describe both a Mumbai user and a Chennai user, which is one reason this page treats published norms as description and our score does not apply a regional norm at all.
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 group, different meaning; our score places these readings against everyone who has scanned here, so read them with these norms in mind.
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. Our score does not split by ancestry, so it cannot 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"; the published norm on this page is the context that does.
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. A European mean is not a universal target; our score does not adjust for this, so read your nose reading with these norms in mind.
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; our pooled score does not split by ancestry, so that norm is the context for your lip reading.
Palpebral fissure angle
Average canthal tilt sits in a distinct range. The eye aperture geometry registers slightly differently; published norms treat this as the population mean, not a deviation.
Lower-face proportion
Lower-face height relative to total facial height clusters in a distinct sub-range. Published norms treat it as the population mean, not 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
The universal percentile here is one pooled reference group: every metric is placed against everyone who has scanned here, not split by ancestry. That pooled read answers one honest question: where do I sit relative to the people who use this tool. It cannot answer where you sit relative to faces with similar ancestry; the published South Asian norms on this page are the context for that. Hiding the pooled number would protect the user from an unflattering read, at the cost of the one comparison we can actually measure.
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. Any published South Asian norm 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 published regional norms as directional — the published Indian norms sample some of these groups more thoroughly than others. Our score does not split by ancestry at all.
- 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. The full report is the plan.
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The $14.99 Looksmax Report scores every metric your photo supports (typically 15 of the 17), each placed against everyone who has scanned here, names your weakest and writes a 30-day plan.
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17 metrics · Multi-ethnic norms · Photos auto-deleted
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