5 Things You Didn’t Know About Machine Learning

Facial recognition technology

Facial recognition, a technology that uses the capabilities of artificial intelligence and machine learning, has become more mainstream in recent years. From the iPhone’s Face ID to security checks at airports, it can be found all around us. But how does it work? Where does machine learning in facial recognition come up? Which industries need this technology? These are just a few of the questions we will be taking a deeper dive into in this blog. 

What are Facial Recognition Technology Systems?

Facial recognition technology can be found in software that has the ability to identify individuals based on their distinct facial features. These systems identify others through an analysis of, for example, the distance between two eyes, the shape of the nose, or the length of the face. By doing so, a unique biometric “faceprint” is formed and stored in a database of known faces. As a result, any person can be identified in less than five seconds, saving a business’s time and resources. 

Facial Recognition and Machine Learning – How Does it Work?

Following are the four processes that facial recognition online performs through the use of machine learning:

  • Face Detection

The software must first detect an individual’s face to get to their identity. In today’s tech-savvy era, most smartphones have in-built face detection systems. This is also the process through which Snapchat filters are applied and the method through which different apps apply special effects to images. 

  • Face Alignment 

Faces that are not directly facing a camera can become difficult for software to recognize. To automate face alignment, facial landmarks are formed. For instance, the top of the forehead and the bottom of the chin. Once this is done, the machine learning algorithm in facial recognition online is trained to recognize these landmarks. This makes it easy to turn a face towards a focal point.

  • Feature Extraction

This step involves the extraction of facial features detected in a face to match them against a database of biometric faces. The process, known as embedding, uses deep convolutional neural networks through which a machine learning algorithm is able to identify a face. 

  • Face Recognition

By using the measurements of each face, machine learning algorithms match them against the database of known faces. The face that comes closest to the measurements is returned as the match.  

  • Face Verification 

In the final step, ML algorithms come up with a final decision stating whether the faces in question match or not. 

Industries That Require Facial Recognition 

  • Banking and financial institutions

Banks and other financial institutions have the crucial task of securing customer data as well as the transactions they make. To secure accounts and to keep out imposters, facial recognition, a machine learning-based solution, is utilized. As a result, only authorized individuals are allowed to access an online account. 

  • E-commerce

E-commerce stores increasingly incur losses due to false chargeback claims. However, facial recognition is being utilized to verify whether the customer is authorized to make the chargeback claim or not. Additionally, similar to banks, e-commerce stores also secure online customer accounts and the financial data provided with the help of facial recognition systems. 

  • Retail stores

Facial recognition equips retail store owners with the perfect method of identifying shoplifters and known criminals that may be entering the facility. This way, fraudsters can be closely monitored and the threat they bring along can be eliminated before any damage is done. 

  • Law enforcement

The uses of facial recognition in the field of law enforcement are countless. Countries like the UAE have invested in this technology to secure subway stations from criminals, verify the identity of drivers breaking traffic rules, detection of criminals on the run, and much more. 

  • Travel sector

Facial recognition systems are widely used by the airline industry to make airport clearance more secure and rapid for passengers. Instead of manually displaying an ID card and passport, travelers can be easily simply verified and onboarded with a face scan. 

Key Takeaways

  • Adoption of facial recognition has become more widespread in recent years
  • Facial recognition has particularly revamped security systems through the use of artificial intelligence and machine learning models 
  • Banking, e-commerce, retail, and the travel sectors are some industries that require the use of this technology to keep fraudulent activities at a minimum
  • Know Your Customers Customer(KYCC) is also a technology used in face recognition 

 

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