Updated: Jan 13
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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:
Advertising: face detection has the ability to make advertising more targeted by making educated guesses at people’s age and gender
Healthcare: face detection can be used to diagnose diseases that cause detectable changes in appearance
Marketing: face detection can be used to count the number of people (affluency) and see if people are smiling or not, young or old, male or female, etc.
Other facial extraction: analyze crowds face features like: eyes color, nose, mouth, skin color, hair style and color, etc.
Top 10 Face Detection API:
Microsoft Azure - Available on Eden AI
The Azure Face service provides AI algorithms that detect, recognize, and analyze human faces in images. Facial recognition software is important in many different scenarios, such as identity verification, touchless access control, and face blurring for privacy. Face detection is required as a first step in all the other scenarios. The Detect API detects human faces in an image and returns the rectangle coordinates of their locations. It also returns a unique ID that represents the stored face data, which is used in later operations to identify or verify faces.
Available on Eden AI
Imagga is a computer vision artificial intelligence company. Imagga Image Recognition API features auto-tagging, auto-categorization, face recognition, visual search, content moderation, auto-cropping, color extraction, custom training and ready-to-use models. Available in the Cloud and on On-Premise. It is currently deployed in leading digital asset management solutions and personal cloud platforms and consumer facing apps.
Google Cloud - Available on Eden AI
Face Detection detects multiple faces within an image along with the associated key facial attributes such as emotional state or wearing headwear. The Vision API can perform feature detection on a local image file by sending the contents of the image file as a base64 encoded string in the body of your request.
Available on Eden AI
Face++ recognition technology can recognize persons' identities automatically from image and video. Our technology is widely used in security, VIP recognition, photo tagging and face login. Face++ detects and locates human faces within an image, and returns high-precision face bounding boxes. Face detection is the first step to analyzing and processing faces, Face++ also allows you to store metadata of each detected face for future use.