Face Detection in Augmented Reality
Introduction
Face Detection (FD) computer technology that is able to identify the presence of people’s faces within digital images. It is a part of object detection.
In order to work, face detection applications use machine learning and formulas known as algorithms to detecting human faces within larger images. It now plays an important role as the first step in many key applications — including face tracking, face analysis and facial recognition.
How face detection works
Face detection applications use algorithms and Machine Learning (ML) to find human faces within larger images, which often incorporate other non-face objects such as landscapes, buildings and other human body parts like feet or hands. Face detection algorithms typically start by searching for human eyes — one of the easiest features to detect. The algorithm might then attempt to detect eyebrows, the mouth, nose, nostrils and the iris. Once the algorithm concludes that it has found a facial region, it applies additional tests to confirm that it has, in fact, detected a face.
- Knowledge-based, or rule-based methods, describe a face based on rules. The challenge of this approach is the difficulty of coming up with well-defined rules.
- Feature invariant methods — which use features such as a person’s eyes or nose to detect a face — can be negatively affected by noise and light.
- Template-matching methods are based on comparing images with standard face patterns or features that have been stored previously and correlating the two to detect a face. Unfortunately these methods do not address variations in pose, scale and shape.
- Appearance-based methods employ statistical analysis and machine learning to find the relevant characteristics of face images. This method, also used in feature extraction for face recognition, is divided into sub-methods.
To help ensure accuracy, the algorithms need to be trained on large data sets incorporating hundreds of thousands of positive and negative images.
Face Recognition and Face tracking
A facial recognition system is a technology capable of matching a human face from a digital image or a video frame against a database of faces.
Face tracking means that the application can, on the one hand, detect that there is actually a face in the image and, on the other hand, follow the face movements.
Recently Those tow Technologies has joined forces with another cutting-edge technology — augmented reality — to create an entirely different family of mobile applications that can have both an entertaining and commercial value.
Field of Usage
AR Advertising
Look in your smartphone as if in a mirror, try on all sorts of L’Oreal makeup and then just have fun with it — make faces, turn your head this or that way, add and remove makeup products to create new glamorous looks. The virtual makeup will move with you, so you will almost “feel” it while in reality, your face will remain clear.
Customer support ( medical context )
When used in research or a medical context, CV algorithms can analyses facial expressions that represent emotions and identify minute changes in pupil diameter and facial muscles that indicate emotions such as joy and despair. These are usually invisible to the naked eye and many are involuntary reflexes. This could help those with social difficulties, for example someone on the autistic spectrum, to interpret reactions in others, let brands research the effect of advertising or even help people to understand their own feelings.
Transport
Passengers in autonomous vehicles wearing AR head-up displays (HUDs) could be alerted to obstacles, traffic conditions and route navigation. Cameras running augmented reality face recognition and analytics technologies could monitor drivers for signs of tiredness and advise them to take a break.
resources
https://www.banuba.com/blog/10-emerging-applications-of-ar-face-recognition
