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HomeTECHNOLOGYHow Deep Learning Facial Recognition Uses Neural Networks?

How Deep Learning Facial Recognition Uses Neural Networks?

In this era where financial scams and online frauds are on the rise, which makes it more significant for businesses to verify their customers’ data and ensure their identity. Innovations in facial recognition have taken biometric authentication and identification to another level, deep learning being the most advanced. The onboarding process has been modified due to KYC and AML procedures.

How ID Verification has Evolved in Recent Years?

Traditionally, banks and other businesses want security for their customers and their data. 

The procedures for data collection and data storage have been modified. Businesses collect data using digital means, gone are when they had to conduct surveys in markets. They can do that through an online form with less time and more response. Customer onboarding was very difficult and tedious, companies had to arrange multiple meetings and manual ID verification was required. A team checks and scrutinizes the documents provided by the customers. It took a long time and the results were not that effective. Humans can’t easily spot fake documents, and thus it gives fraudsters an advantage to join a business, imposing as a legitimate customer. 

But now is the time for businesses to look into technologies for recognizing and verifying their clients, one of which is deep learning facial recognition.

What is Deep Learning Facial Recognition?

Biometric authentication technology incorporates software that identifies individuals through their biological and physical attributes. Biometric verification has been in the system long ago, for example policing authorities use it for criminal investigation. They mostly use fingerprint scanners and DNA. Also, the high-tech labs that require stringent security use IRIS biometrics. While biometric facial recognition is used for identifying persons.

Facial recognition technology is an automated solution that recognizes human faces from faces in images, videos. The technology is mostly used for biometric authentication or categorization. It scans the patterns in facial images to find a match. Online facial recognition technology rebuilds a 3D image using the data on the 2D image to discover similarities in two matches.  

In the first step, faces are removed from the backgrounds (colored or simple) after locating them. The face and non-face areas are identified, and then the face is segmented from the image based on its location and scale.  Then the canonical coordinates are automatically aligned using the face normalization tool. Making use of the special attributes such as distance between eyes, ears shape and size, nose position, and jaw outline. Identical facial characteristics are picked out by the normalization process. 

Drawbacks of using Traditional Facial Recognition System

Traditional facial recognition solutions lack face processing, later on, the incorporation of deep learning makes use of this step. Deep learning technology has helped facial recognition technology highlight more details and the conversion of 2D images into 3D representation. The difference among the facial features of individuals is created through the extraction process. The attributes in the pictures are converted into vector representation using geometric and photometric points.

The last step is one where both the samples are compared, and online face verification matches the patterns extracted from the images to the databases. When a person is known to the database, the face patterns are matched and the person is marked as verified. The process is also known as know your customer (KYC) verification. In traditional facial recognition software algorithms are designed by humans making it partially dependent on the system protocols. Deep learning software is designed for self-learning that can extract and compare data sets using the neural network. 

Wrapping It Up

The accuracy of the software increases as it gets more data sets. The neural networks make it easier for facial recognition software to analyze biometric patterns. 

In KYC verification, the software matches the person against the photo ID to determine the similarity. Using facial recognition in the KYC process has proved much beneficial to remain compliant with the KYC and AML regulations. The robust ID verification is now possible with more accurate results. It takes just seconds to identify an individual, making the process smoother. 

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