Do you often scan your face to unlock your smartphone instead of typing in your password? The unique hands-free technology that you are taking advantage of is called Facial Recognition Technology (FERET). The leading smartphone company Samsung first introduced this technology in their Android phone launch in 2012. Then, in 2017, the Apple iPhone X further expanded FERET usage beyond just unlocking phones. They employed it to serve as a single point of authentication for different smartphone functions like downloading the apps, mobile payments, etc. This trend ultimately replaced the traditional password culture and made the smartphone authentication experience completely hands-free!

Facial Recognition Technology comprises of both hardware and software components that work hand-in-hand to identify and validate facial images. The FERET Hardware component takes care of capturing the facial image and providing the appropriate light and storing the images. The FERET Software component will then interact with the hardware component to retrieve the captured image and apply facial recognition models and algorithms to identify & validate the facial image against the template image stored in the hardware. 

Let's find out how this FERET software validates a facial image. It is already clear that our brain recognizes familiar faces by correlating different facial features to identify an already known face. The Facial Recognition algorithm also applies the same correlation principles to recognize facial image data points, and maps this data with template facial data to match and validate if both of the face images are the same. This validation algorithm follows a logical flow of events.

It is vital to know the FERET software's underlying technology before diving into the actual facial recognition process. FERET software applications utilize many facial recognition software libraries that process 2D and 3D facial images. One such 2D facial recognition library is called Cascade Classifiers, and they can identify different facial features of animals and humans. These cascade classifiers are data points that precisely represent the location of facial features, feature type, and their relative distance from each other. 

Steps of the Facial Recognition process:

Face detection: The FERET software component reads the facial image file taken by a FERET hardware component (camera) and identifies the region of the image with the facial features.

Face Normalization: After the face is detected in the image, the image is converted into grayscale mode and is cropped to focus only on the facial region for further analysis.

Feature Extraction: The normalized facial region is then analyzed to identify and extract the different features of the face like the eyes, nose, eyebrows, and lips, etc. These extracted features are represented by a unique data matrix that is distinct for each distinct facial image. No two data matrices derived from facial images of two different people will ever match, even when they are related or biological twins! It is just like one's fingerprint impression. It's unique and distinct.

Face Recognition: The extracted facial feature data matrix can be compared against any template image’s facial feature data matrix. These matrices represent numerical values (Specifically, the distance coordinates between the features). The outcome of these calculations reveal if these two matrices are the same or different. Based on this result, the algorithm concludes if the two faces match or not.

Based on the explanation above, facial recognition technology taps into the fact that every individual's facial structure and features can be treated as a unique identifier and can undoubtedly replace traditional password authentication methods. Apart from basic smartphone authentication, facial recognition technology is also widely used in many other daily applications such as in airport security surveillance, student class roll verification, social media picture tagging, airport departure gate check-in, and local law enforcement assistive methods to identify and solve missing person and fugitive cases. 

According to Juniper Research Firm, by 2019, 96 million mobile phones with FERET hardware were deployed globally, and this number will reach 800 million by 2024. On the other hand, FERET software features are estimated to be deployed to 1.3 billion smartphones by 2024. It is clear that Facial Recognition Technology will play a huge part in our future world as more and more industries take advantage of this technology.