Intelligent skin analysis with Herbsom

This project was carried out as part of the TechLabs “Digital Shaper Program” in Münster (summer term 2021).


To suggest the most suitable active ingredient composition for a skin cream, we developed, at the request of the start-up Herbsom, an algorithm that enables skin analysis based on facial photos. After a potential customer has uploaded a photo of the face, the skin type is categorised. This categorisation then allows a conclusion on which ingredients should be added to the base cream for addressing the individual skin characteristics and problems.


Our project idea comes from the Münster-based start-up Herbsom, which launches customisable natural cosmetics on the market. The founders noticed that many people find it difficult to classify their own skin type.


Overall, we used Python (Pytorch/Pandas/Fastai etc.) via Jupyter Notebooks (Colab) as well as Slack and Notion to organize our work progress.


Herbsom wanted a program that would make it easier for customers to assess their skin type and choose the right products. Our end result makes this possible primarily for wrinkle and pimple classification. In this cases an accuracy of more than 90% is attained. Extending our results to categories that can also be represented on a scale from to (e.g. how shiny is the skin) seems equally feasible. Due to the limited data and time given, the categorization of skin types was not achievable with sufficent accuracy.

The team

Celina Tschorn (AI): Data cleaning, Learner, Blogpost


Justin Hellermann

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