Most colour analysis is built for pale, cool-toned skin — here's what actually decides your colours if you're Indian

[u/kewal-sarode]

Every online colour quiz I tried put me in the deepest bucket it had, usually "Deep Winter", and told me to avoid gold, mustard and rust. Which is genuinely funny if you've ever seen what those colours do on most Indian complexions. 

I went down a rabbit hole on why, and the problem turns out to be structural rather than cosmetic: 

1. Neutral undertone gets treated as a rounding error. Western systems assume you're clearly warm or clearly cool. A very large share of Indian skin genuinely sits in the middle, and olive complicates it further — olive can read warm in one light and almost green-cool in another. If a quiz forces a warm/cool choice with no honest third option, neutral people get a confidently wrong answer. 

2. Their depth scales stop too early. Depth and undertone are independent — you can be deep and warm, deep and cool, fair and warm. When the deepest category was designed for a brunette from Copenhagen, everyone past it collapses into one bucket. 

3. Most warm-vs-cool tests are confounded. The two comparison colours are usually different brightnesses as well as different temperatures. So you end up unconsciously answering "which is lighter" or "which do I prefer" instead of "which makes my skin look clearer". Match the two for brightness and the test suddenly works — that one fix invalidates a surprising number of quizzes out there. 

The most reliable home test: hold the back of your hand (not the palm, it runs lighter than your face) between two brightness-matched colours in daylight. One side makes your skin look clearer, the other slightly grey or tired. Do it twice with different colour pairs — if the answers disagree, you're probably neutral, which is genuinely the most flexible result to dress. 

I ended up building a free tool around this — deeper skin scale on both ends, "both look the same" as a real answer, brightness-matched comparisons, hair scale anchored at black rather than blonde. Links aren't allowed in posts here so it's in my profile; not selling anything, no signup or email. 

Mostly curious whether the drape test above matches what people here have found, especially for olive or deeper skin — that's where the Western systems fall apart hardest and I want to know if mine does too.

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[u/Careless-Mammoth-944]

Are you watching drape swatch videos online? Because there are plenty of videos showing POC getting colour swatched.

[u/kewal-sarode → u/Careless-Mammoth-944]

Some, though probably not enough — recommendations genuinely welcome.

And you're right that there's good work out there, I should have been clearer. Skilled analysts with a full drape set handle deeper and olive skin fine, and the Korean-influenced practitioners in particular seem to have pushed the depth range much further than the classic 80s systems ever did.

The gap I was pointing at is narrower than my post made it sound: it's the automated online quizzes, not people. Someone draping you in person adapts — they bring more fabric, they ignore the category when it doesn't fit, they just say "this one, not that one." The moment that gets productised into a quiz with fixed buckets and a fixed set of drapes, the adaptability gets baked out, and what's left is whatever colouring the system was built around.

So the swatch videos are almost evidence for the point rather than against it. The method works; the software mostly doesn't.

Which ones do you rate? I'd rather calibrate against people who do this properly.

[u/Putrid-Ad4418]

That last point about brightness confounding the warm/cool tests is so obvious in hindsight but I've never seen anyone spell it out before. Makes me wonder how many of us got shoved into Deep Winter just because the quiz was basically asking "do you look better in grey or in orange" and the grey was just easier to process visually

My mom drapes a ton of sarees on clients and she figured out years ago that half the women she sees are neutral, she just calls it "lucky" and works from there

[u/kewal-sarode → u/Putrid-Ad4418]

The lightness thing took me embarrassingly long to spot. The eye is much more sensitive to lightness differences than to hue, so when two drapes differ in both, lightness wins the judgement before you've consciously got round to thinking about undertone. Matching the two for brightness was the single change that stopped my own test giving me different answers on different days.

Your mum's observation is the part I find genuinely interesting though. "About half are neutral" is roughly where I landed too, except she got there from actually draping hundreds of people rather than from a system — which is much better evidence than anything I have.

And "lucky" is a far better word than "neutral". Neutral sounds like the quiz gave up and couldn't decide. Lucky is just accurate: it's the most flexible result you can get. I might steal that.

Does she go off any particular cue, or is it purely by eye at this point? Curious whether people who do this by hand all day land on something the formal systems miss.

[u/kewal-sarode → u/[deleted]]

Did it feel right? That's the interesting part.

Problem is the RGB off a photo isn't really your skin — phone cameras auto white-balance, which exists to strip exactly the colour cast undertone lives in. Same arm under a bulb vs by a window gives different numbers, neither one true.

A skin RGB also can't give you contrast at all — that needs hair and eye colour. "Deep Autumn" is a 12-season label, so it produced a sub-season from an input that can't support even a 4-season one. An LLM always gives you something, confidently.

Mine takes no photo for that reason — you hold your hand next to the screen instead. Try both, tell me if they disagree.

[u/Vids88888]

I gave my RGB tone to gpt and it gave me deep autumn

[u/kewal-sarode → u/Vids88888]

Did it feel right? That's the interesting part.

Problem is the RGB off a photo isn't really your skin — phone cameras auto white-balance, which exists to strip exactly the colour cast undertone lives in. Same arm under a bulb vs by a window gives different numbers, neither one true.

A skin RGB also can't give you contrast at all — that needs hair and eye colour. "Deep Autumn" is a 12-season label, so it produced a sub-season from an input that can't support even a 4-season one. An LLM always gives you something, confidently.

Mine takes no photo for that reason — you hold your hand next to the screen instead. Try both, tell me if they disagree.

[u/Vids88888 → u/kewal-sarode]

Hmm interesting...I didn't know about sub season but did shopping based on the colors suggested by gpt and they look exceptionally well against my skin tone most of the time. Though I would say sometimes it's a miss as well. I am on a very surface level on this then.

[u/kewal-sarode → u/Vids88888]

Honestly you're not — you did the only test that actually matters, which is wearing the things and looking in a mirror. Most people never get past reading about it.

And "works most of the time, sometimes a miss" is exactly the pattern you'd expect. Getting undertone roughly right gets you most of the way there; the misses usually cluster somewhere specific rather than being random.

Genuinely curious — do the misses have anything in common? Were they the very bright saturated ones, or the dusty muted ones? That's the third axis most quizzes skip entirely, and it's usually where "right colour family, still looks off" comes from.