How can we improve the donor selection to increase the resemblance between receptors and their offspring? It’s necessary to implement new algorithms including biometry distance measurements (Fig.1.1) across family generations to improve donor selection according to the facial resemblance.

What is Fenomatch?

Fenomatch is a facial matching tool that helps doctors find the right donor for each parent.

The Artificial Intelligence algorithm compares the parent’s face with the faces of the potential donors, focusing on phenotypic traits- those which are passed on to our children via DNA.

Your doctor can choose the donor with the greatest facial resemblance.

The platform also checks other phenotypic traits like eye colour, hair colour, ethnicity, skin tone, etc. as well as generic compatibility and blood type.


Filter donors by their facial resemblance to the patient

Use AI to compare over 12,000 biometric data points

Check genetic compability

Check donor/patient compability with just one click

Minimise the risk of human error

Automatically verifying traits such as ethnicity and gender, and providing traceability

Provide peace of mind to the patient

A certificate verifies the use of the latest techology in the donor selection process

Save time and improve efficiency

Give your clinical team objective criteria to make the right decision

Find the right donor

Work with your own gamete bank, or connect with others

Fenomatch in numbers

We works over

clinics around the world

Outtechnology is used on


Algorith with over

0 million
lines of code


This platform is built on 100% secure technology.


A total of 864 subjects (age 18-60) from 108 families (Fig.1.2) were included with their participant agreement (73% training – 27% validation).

All images correspond to 21-50 years old subjects, with a gender distribution 1:1.

Scripts in Python and R were developed to classify 12,208 Facial distances of each picture using K-means algorithms to study facial similarity objects such as face shape, bone structure similarities, eyes symmetry or lips and cheek distances. (Fig.1.3)

80 Gb RAM and 60 computing processors were used intensively.

Artificial Neural Networks methodologies were finally used to find a predictable pattern.


The new algorithm is stable and can reproduce results when iterating validation data.
The new prediction model based on biometric distances results is 86,66% (σ2 47.88) if we require all images must be in the right order to get a positive result.

The accuracy results improve performance up to 96% when the right order is just required in the 5 images with more resemblance.

Comparison between images is independent of the subject gender providing same accuracy results for gender-crossed images as for same-gender.

As final extra testing, when the test was made by the computational algorithm and also by a human team (10 individuals with a majority vote criteria), humans results are 98% in line with the new algorithm.


When doing the same testing with the two most used existing open python scripts based on facial recognition average results provide 56.88% of accuracy (σ2 638.98) in ordering subjects right.

When comparing gender-crossed images test shows just a 23.38 (σ2 89.23) of accuracy.

This comparison result makes sense since facial recognition techniques are not  intended to compare genealogical similarities, but only have the purpose of biometric identification of a person.


We can conclude that Fenomatch new algorithm is valid as it has great accuracy in its results and it has widely improved results in the current state of the art.

The Fenomatch algorithm should be used as a decision support application that helps the medical team decide on a donor from among those who already meet the medical and genetic criteria in order to guarantee to find the donor that most resembles the receptor’s family.

Other approaches based on facial recognition techniques does not show enough accuracy to be used for donor selection purposes.

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