Identify makeup layering using AI

Below is a free classifier to identify makeup layering. Just upload your image, and our AI will predict what makeup products to layer for the desired look - in just seconds.

makeup layering identifier

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Get started

    import nyckel
    
    credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
    nyckel.invoke("makeup-layering", "your_image_url", credentials)
                

    fetch('https://www.nyckel.com/v1/functions/makeup-layering/invoke', {
        method: 'POST',
        headers: {
            'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
            'Content-Type': 'application/json',
        },
        body: JSON.stringify(
            {"data": "your_image_url"}
        )
    })
    .then(response => response.json())
    .then(data => console.log(data));
                

    curl -X POST \
        -H "Content-Type: application/json" \
        -H "Authorization: Bearer YOUR_BEARER_TOKEN" \
        -d '{"data": "your_image_url"}' \
        https://www.nyckel.com/v1/functions/makeup-layering/invoke
                

How this classifier works

To start, upload your image. Our AI tool will then predict what makeup products to layer for the desired look.

This pretrained image model uses a Nyckel-created dataset and has 20 labels, including Bold, Colorful, Dewy Finish, Dramatic, Evening, Everyday, Full Coverage, Glam, Glitter and Heavy.

We'll also show a confidence score (the higher the number, the more confident the AI model is around what makeup products to layer for the desired look).

Whether you're just curious or building makeup layering detection into your application, we hope our classifier proves helpful.

Need to identify makeup layering at scale?

Get API or Zapier access to this classifier for free. It's perfect for:



  • Virtual Makeup Try-On: This feature enables cosmetics brands to offer customers an augmented reality experience where users can virtually apply layers of makeup. The false image classification function distinguishes between genuine makeup application and simulated effects, ensuring that users receive an accurate representation of how products will look on their skin.

  • Social Media Content Moderation: Influencers and brands can use this function to filter out misleading or altered images in their feeds. By identifying false makeup layering in photos, it helps maintain authenticity and clarity when promoting products on social platforms.

  • Fraud Detection in E-Commerce: Online retailers can implement this technology to verify product reviews and images uploaded by customers. By flagging photos that exhibit false makeup layering, businesses can prevent deceptive marketing practices and protect their brand reputation.

  • Makeup Tutorials Quality Assurance: Cosmetic education platforms can use this function to enhance the quality of makeup tutorials. By identifying false layers in tutorial videos, they ensure that viewers are learning techniques that reflect genuine makeup application.

  • Influencer Marketing Verification: Brands can utilize this function to analyze influencer-generated content for authenticity. By identifying images with false makeup layering, companies can make informed decisions about partnerships and promote transparency in their marketing efforts.

  • AR Makeup Application Development: Developers of augmented reality makeup apps can integrate this functionality to enhance user experience. By automatically distinguishing between real and digitally applied layers, the app can provide users with more realistic simulations of makeup products.

  • Cosmetic Brand Research & Development: Cosmetic companies can leverage this function for market research by analyzing customer images and preferences. By identifying false make-up layers in consumer photos, brands can gain insights into makeup trends and adjust their product development accordingly.

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