5 Different Types of Facial Recognition Technology and Their Evolution
The current technology never ceases to amaze people with amazing innovations that not only make life simple but also bearable. Face recognition has over tie proven to be 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 world’s number one form of identification and verification tool.
If you are to use the face ID technology on your mobile phone or smart devices, it is only imperative that you gain a deeper understanding of not just how it works. You should also strive 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 types of facial recognition technologies in their order of evolution.
Holistic matching method
The holistic matching type of facial recognition was pioneered in the periods leading to the 21st century. And 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.
Just as the name suggests, Holistic matching facial recognition technology took into account the whole face region and used different types of data sets to come up with hits and misses. It would have such unique features as the distance between distinct facial features like the eyes. It would also employ the pattern recognition technology that helped distinguish between different angles of equally distinguishable facial features like the nose, eye and lip curves.
It would face a huge challenge that inhibited its mass applicability. The fact that it used 2D technology meant that it the holistic technology would help with facial recognition when images were tilted, or someone expressed different facial recognition from the image on the system database.
The feature-based type of face ID verification would be introduced later as an improvement of the holistic matching technology. To a large extent, this new type of facial recognition sought to capitalize on the flaws of its predecessor. For instance, while the predecessor tool used the rudimental facial features like the eyes, nose, and mouth to derive unique facial features, the feature based 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 the geometric and structural classifier tools to identify more uniquely identifiable facial features. These tools helped the system come up with such features as facial edges, curves and even lines. It would go on to 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 the 2D technologies. This means that slight changes in facial expressions and 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 most probably report it as a miss. The introduction of its successor to the industry would reveal that the second dimension technology wasn’t its only inhibiting factor. The feature-based face id tool was also limited in the number of facial features it used for comparison.
While there are several and differently abled facial recognition tools in use today, they all are broadly referred to as the hybrid types of face id systems. These took advantage of the successes reported by both the holistic matching and feature based versions of the face identification technology to come up with a more sophisticated and more accurate face recognition tools.
Their development also takes into consideration 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 line nose and eyes or the facial lines and curves, it goes on to identify more depth with the facial images and identify more unique features referred to as facial nodal points.
These can now be used to positively match captured images with the ones in databases regardless of the angle of the photo or facial expressions exemplified. Today, there are over 80 distinctive nodal points with every face that this technology uses for identification and verification purposes.
Skin texture analysis
Skin texture analysis is a progressive recognition tool that threatens to break away from the larger hybrid technology. The technology captures the unique lines, spots and patterns on an individual’s skin and analyses them for a match.
This represents a more advanced form of facial recognition that hopes to tame the errors associated with facial expressions changes or makeup distortion. This type of face id verification only captures the individual’s shape of the head while ignoring such accessories as glasses or makeup. It particularly uses low-resolution electrics to capture the thermal signatures of the face.
It is true that the technology we enjoy today has come a long way and the only thing we can appreciate those that invented is to use the technology to our advantage. With that insight, facial recognition technologies have as well come a long way and have only been able to achieve this success after numerous structuring and modification attempts. The holistic approach type of face id, as well as the feature based method, can today be viewed as stepping the stones to the achievement of the now cutting-edge facial recognition tools that every smart device manufacturer hopes to install in their new gadget.