Big Boss Studio, for COTY, 2022

Lancaster LDIAG

In-store skin diagnosis on iPad, with image analysis and a personalised routine.

Role: Lead developer
Stack: React Native, React, TensorFlow, Perfect Corp
Expertise: Mobile, AI

01<Specs />

HEADER

CAMERA

FACE

ANALYSIS

CAPTURE

820 × 1180 · 8 col.

02<Wireframe />

LancasterLDIAGScreen.tsx
1export function LancasterLDIAGScreen() {
const items = useFeed();
return (
  <Screen>
    <Camera position="front" />
    <Mesh face={points} />
    <Analysis zones={zones} />
    <Routine products={advice} />
  </Screen>
);
11}

03<Code />

9:41

Lancaster LDIAG

04<Interface />

1frame

2analyse

3capture

05<Interactions />

linear · 1600 ms

06<Animations />

01 · Context

Guide each customer to the right Lancaster products based on her skin type, with an in-store advisory tool that respects luxury codes.

02 · The problems to solve

[The first problem to solve.]
[The second.]
[The third.]

03 · Features

Skin diagnosis

Imperfections and signs of ageing analysed with Perfect Corp technology.

One score, five gauges

Hydration, radiance, wrinkles, spots and firmness, summed up in a Self Renew score out of 100.

The routine that goes with it

THE beauty routine recommended for each skin type.

In store, on iPad

An app reserved for points of sale.

04 · The technical solution

[The technical solution, in one strong sentence.]

iPad app reserved for points of sale. Analysis of imperfections and signs of ageing with Perfect Corp and TensorFlow, then a personalised skincare routine recommendation. React Native interface, React back office.

[Building block]: [Its role in the solution.]
[Building block]: [Its role in the solution.]
[Building block]: [Its role in the solution.]

05 · Results

[Figure]

[What it measures]

[Figure]

[What it measures]

[Figure]

[What it measures]

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