Current technology never ceases to amaze people with innovative innovations that make life not only simple but also bearable.
Face recognition has proven to be the least intrusive and fastest form of biometric verification.
This explains its quick adoption by different technological device manufacturers and security system developers, where pundits argue that it might soon become the world’s number one form of identification and verification tool.
If you are going to use face ID technology on your mobile phone or smart device, it is imperative that you gain a deeper understanding of how it works.
It would be best if you also strived to learn how it evolved from rudimental sketches to the current sophisticated tool that every device manufacturer seeks to incorporate into their next production.
Here are three types of facial recognition technologies in their order of evolution.
1. Holistic Matching Method
The holistic matching type of facial recognition was pioneered in the periods leading to the 21st century.
While it was a significant stride in the development of the face-catching system, it can now be considered a formative but still rudimental face-identification tool whose advancements by different institutions led to the birth of the all-new technologies being used today.
Its successors would borrow heavily from its original form to develop more sophisticated systems by adding more uniquely identifiable data to the existing ones.
As the name suggests, Holistic matching facial recognition technology takes into account the whole face region and uses different types of data sets to determine hits and misses.
It would have such unique features as the distance between distinct facial features like the eyes.
It would also employ pattern recognition technology that helped distinguish between different angles of equally distinguishable facial features, such as the nose, eye, and lip curves.
It would face a huge challenge that inhibited its mass applicability. Using 2D technology meant holistic technology would help with facial recognition when images were tilted, or someone expressed different facial recognition from the image on the system database.
2. Feature-based Method
The feature-based type of face ID verification will be introduced later as an improvement in the holistic matching technology.
This new type of facial recognition sought to capitalize on its predecessor’s flaws.
For instance, while the predecessor tool used rudimental facial features like the eyes, nose, and mouth to derive unique facial features, the feature tool sought to avoid this flaw and capitalize more on the facial features that were hard to distort with different camera angles of facial expressions.
This new type of facial recognition employed geometric and structural classifier tools to identify more uniquely identifiable facial features.
These tools helped the system develop features like facial edges, curves, and lines.
It would record a higher facial recognition score when compared with the holistic approach.
But it, too, had its crippling limitations that inhibited its mainstream application. For instance, it would still lay too much emphasis on 2D technologies.
This means that slight changes in facial expressions and the angle of the imagery led to huge shortfalls in matching these faces.
For example, if the program developer had a portrait photo of an individual but the face ID system took a symmetrical facial image of the same person, the feature-based tool would probably report it as a miss.
The introduction of its successor to the industry would reveal that technology’s second dimension wasn’t its only inhibiting factor.
The feature-based face ID tool was also limited in the number of facial features used for comparison.
3. Hybrid Types
While several differently-abled facial recognition tools are in use today, they are all broadly referred to as hybrid face ID systems.
These took advantage of the successes reported by both the holistic matching and feature-based versions of face identification technology to develop more sophisticated and accurate face recognition tools.
Their development also considers and improves on the failures of the two predecessor technologies.
For instance, instead of using 2D technologies in matching faces, hybrid technologies use 3D technology.
Additionally, instead of using just the distinctly identifiable facial features, such as the nose and eyes or the facial lines and curves, it goes on to identify more depth with the facial images and more unique features referred to as facial nodal points.
These can now be used to positively match captured images with those in databases regardless of the photo’s angle or facial expressions.
Today, there are over 80 distinctive nodal points with every face this technology uses for identification and verification.
4. Skin Texture Analysis
Skin texture analysis is a progressive recognition tool that threatens to break away from the larger hybrid technology.
The technology captures an individual’s unique lines, spots, and patterns on the skin and analyses them for a match.
5. Thermal Cameras
This represents a more advanced form of facial recognition that hopes to tame the errors associated with facial expression changes or makeup distortion.
This type of face ID verification only captures the individual’s head shape, ignoring accessories such as glasses or makeup.
It particularly uses low-resolution electrics to capture the thermal signatures of the face.
Bottom line
It is true that the technology we enjoy today has come a long way, and the only way we can appreciate those who invented it is to use it to our advantage.
With that insight, facial recognition technologies have also come a long way. They achieved this success only after numerous structuring and modification attempts.
The holistic approach type of face ID and the feature-based method can today be viewed as stepping stones to achieving the new cutting-edge facial recognition tools that every smart device manufacturer hopes to install in their latest gadget.
