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In this article, we are going to see how we can easily integrate a Face Detection engine in your project and how to choose and access the right engine according to your data.
Face detection is AI-based computer technology that is used to extract and identify human faces from digital images. Face detection technology uses machine learning and algorithms in order to extract human faces from larger images; such images typically contain plenty of non-face objects, such as buildings, landscapes, and various body parts.
The earliest pioneers of facial recognition were Woody Bledsoe, Helen Chan Wolf and Charles Bisson. In 1964 and 1965, Bledsoe, along with Wolf and Bisson began work using computers to recognise the human face.
It wasn’t until the late 1980s that we saw further progress with the development of Facial Recognition software as a viable biometric for businesses. In 1988, Sirovich and Kirby began applying linear algebra to the problem of facial recognition.
In 1991, Turk and Pentland carried on the work of Sirovich and Kirby by discovering how to detect faces within an image which led to the earliest instances of automatic facial recognition.
Face Recognition Grand Challenge (FRGC) was launched in 2006 in order to promote and advance face recognition technology designed to support existing face recognition efforts in the U.S. Government. The FRGC evaluated that the new algorithms were 10 times more accurate than the face recognition algorithms of 2002 and 100 times more accurate than those of 1995, showing the advancements of facial recognition technology over the past decade.
Since the 2010s, Facebook, Apple, Amazon, Google, and other big tech companies developed their own Face detection engines, and face detection is democratized in numerous fields.
You can use Face Detection in numerous fields, here are some examples of common use cases:
When you need a Face Detection engine, you have 2 options:
The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Face Detection. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.
Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Face Detection engines with a provider as a simple parameter. With only a few lines, you can set up your project in production:
Here is the code in Python (GitHub repo) that allows to test Eden AI for face detection:
Eden AI also allows you to compare these engines directly on the web interface without having to code:
There are numerous NER engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate NER technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.
In this article, we explain how the mapping between the input language and the languages supported by the providers is performed to facilitate access to one of our AI engines.