The Impact of Machine Learning on Facial Recognition Technology

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The rapid evolution of digital transformation has created better opportunities for online businesses to perform secure and swift identity verification of their customers. The advent of Artificial Intelligence and modern means for biometric authentication has brought new innovation to the table. Facial biometrics have been in use for quite a long time since 1960 when with the help of the RAND tablet, facial recognition was performed manually. This early technology was a computational machine with minimal graphics and was refined over time with new advances coming to the market. 

Facial recognition was not widely popular until the breakthrough of machine learning and related subfields during the 2010s. This paved the way for scaling machine learning algorithms at large with purpose-built services. This creates room for improvement in service delivery times and accuracy since manual labor is error-prone and slow.  According to a report published by Gartner Summits, by the year 2020, almost 85% of customer-business interactions will be fully automated without a human. 

Facial Recognition Market

With biometrics such as fingerprints sensors, and retina scanners making their way into the market, facial recognition services have also picked up the pace. Facial biometrics are in popular demand because of the ease of accessibility they provide. By the year 2020, the market for facial recognition services is expected to reach a revenue of almost $7.7 billion due to its applications in a wide range of commercial needs. Some use cases include surveillance systems at airports, patient verification in hospitals and clinics, and customer identification for online businesses.  

How does Machine Learning Help?

The term machine learning corresponds to the computer’s learning information and then performing operations according to the instructions fed to them. The inspiration comes from neural networks inside the human brain which naturally process incoming data into information. Artificial Neural Networks (ANNs), which are the building blocks of machine learning algorithms, are created based on the concept of natural processing and work on the same principle of human intelligence.

These algorithms, also called models, are initially trained by using a set of examples and they are employed for verification purposes. Facial recognition technology when powered with these models produces remarkable accuracy for detecting facial features with quicker turnaround times. The main reason for machine learning becoming increasingly popular is because of two reasons: (i) Large Datasets can be accessed over distributed networks anytime and anywhere with a quick search operation (ii) Computing power becomes relatively cheap since resources are efficiently utilized.

Prevention of Facial Spoofing Attacks

Apart from improved accuracy and faster operations with face recognition, anti-spoofing is another benefit of incorporating machine learning. The technology to prevent spoofing attacks is emerging and could create better opportunities for safeguarding sensitive information. 

Traditional facial recognition systems are good but not that great since they could be easily deceived using dubious means. Some facial identity frauds which could bypass simple face detection technology are listed below:

  • 3D Masked Attacks
  • Screenshot Photos
  • Hyperface Attacks
  • Eye-cut Photos
  • Video Replay Attacks
  • Distorted Images

These attacks could provide unauthorized access to sensitive information and could prove fatal for a company’s capital and market reputation, if not stopped. That is why the need to prevent facial spoofing becomes the need of the hour. By combining AI and facial recognition, impersonation attempts could substantially be reduced using 3D depth sensing and liveness detection. A few takeaways are listed below:

  • Fraud Prevention
  • Reduce Identity Theft
  • Protection of Sensitive Information
  • Identifying Paper-based Photos
  • Accurately detecting Facial Masks

The Future of Facial Recognition

Facial identity has become a source of popular authentication and is already contributing to online identity verification. Online payments, smartphone unlocking, and opening online accounts are current use cases which are current uses of facial recognition solutions. 

Facial biometric technology has applications in numerous fields including security and surveillance, the medical sector, e-commerce and retail, online banking, and social media logins. Future opportunities exist in assessing facial micro-expressions or emotions using deep learning algorithms. This can help in real-time monitoring and identification of customers for enterprises and other relevant industries.  

To sum it up, face recognition when paired with machine learning 

is a reliable source of preventing facial spoofing attacks and performing secure customer identification.